Method, System and Business Model for a Buyer&#39;s Auction with Near Perfect Information Using the Internet

ABSTRACT

A methodology, system and business model are disclosed for facilitating a fully automated buyer&#39;s auction in which the major types of transaction costs are significantly reduced by providing the buyer and the sellers with near-perfect information about one another, including information about buyer preferences and competing sellers&#39; offers. The system implements a buyer&#39;s auction with multidimensional bidding that minimizes market intelligence, search, bargaining and transaction execution costs and thus creates more competitive, frictionless markets. Buyers and sellers can efficiently conduct the buyer&#39;s auction within a unified environment, thereby minimizing buyer integration costs as well. The buyer&#39;s auction generates commercially marketable proprietary information and a revenue stream for the auctioneer providing such a service.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/723,140, filed Dec. 20, 2012, which will issue on May 27, 2014 asU.S. Pat. No. 8,738,463, which is a continuation of U.S. patentapplication Ser. No. 13/154,219, filed Jun. 6, 2011, which issued onDec. 25, 2012 as U.S. Pat. No. 8,341,033, which is a continuation ofU.S. patent application Ser. No. 12/512,880, filed Jul. 30, 2009, whichissued on Jun. 7, 2011 as U.S. Pat. No. 7,958,013, which is acontinuation of U.S. patent application Ser. No. 12/029,459, filed Feb.11, 2008, which issued as U.S. Pat. No. 7,584,124 which is acontinuation of U.S. patent application Ser. No. 09/350,983, filed Jul.9, 1999, which issued as U.S. Pat. No. 7,330,826. All of these priorapplications are incorporated by reference herein in their entirety.

FIELD OF THE INVENTION

The present invention relates to creating a buyer's auction withnear-perfect information on the World Wide Web. More particularly, thepresent invention is directed to: (1) a single buyer-multiple sellerelectronic auction methodology in which multi-attribute adjustments tobuyer requests or seller offers are made in real time in response tonear-perfect information for both the buyer and the sellers; (2) acomprehensive, unified system that minimizes the entire chain oftransaction costs—from first desire to buy, through education, search,bargaining, and finally to the sale itself; and (3) a business model toproduce and sell near-perfect proprietary information about the buyerand the sellers that derives directly and exclusively from the auctionprocess. The total effect is to produce near-perfect, frictionless,competitive markets.

BACKGROUND OF THE INVENTION I. Introduction

Traditional markets are burdened by many forms of inefficiency, whichcan be classified into two broad categories. The first category consistsof the costs incurred before and during a transaction. This includes theresources spent learning about products, finding trading partners,negotiating terms, and consummating the transaction. Also included inthis first category of costs is the waste that results when thetransaction that is consummated is not the one that creates the mostvalue. The second category consists of costs incurred after thetransaction is consummated, for example the costs of monitoringperformance or renegotiating terms. This invention is primarilyconcerned with reducing costs in the first category to the maximumdegree, thereby creating near perfect, frictionless markets.

The transaction costs incurred prior to and during the transaction canbe further divided into five types, each corresponding to an informationprocessing problem. This invention is focused specifically on attackingeach of these costs.

First are the market intelligence costs. For buyers, gathering marketintelligence means learning about the products that are available anddoing research about product attributes. For sellers, marketintelligence consists of information about what offerings consumers arelikely to demand and about what potential competitors are offering.

Second are the search costs. Buyers would like to identify all thesellers who can potentially meet their needs, while sellers would liketo reach all the buyers whose demands they can potentially meet.

Third are the bargaining costs. The particular buyer and seller involvedin each transaction must exchange enough information to be able tostructure a personalized, customized deal that creates maximum value forthemselves.

Fourth are the sellers' transaction execution costs. An ideal mechanismwould reduce these costs, allowing the savings to be either appropriatedby the seller, or shared with the buyer, or with a broker-agent (alsocalled an “infomediary” in the modern e-commerce context).

Fifth are the buyer's integration costs. The problem of integratinginformation from disparate and disconnected sources may itselfdiscourage buyers and sellers from becoming perfectly informed.

When all these costs are almost zero, it is said that there isnear-perfect information.

In the past, markets with near-perfect information have been possible inonly very limited settings outside of economics laboratories. For mostconsumer items, in particular, the cost of near-perfect information hasbeen too high relative to its value to make its achievement worthwhile.The result in traditional markets has been a loss of value to bothbuyers, who fail to find their most preferred goods, and sellers, wholose customers they might otherwise have acquired.

However, with the advent of the Internet, the falling costs of finding,communicating, transferring and storing information is beginning tochange markets dramatically. The Internet has enabled buyers and sellersto meet and transact trade in “virtual” markets. Internet-based venturesare taking advantage of the falling cost of communication to create newmarkets of increasing scope and scale that would have been unimaginablebefore the advent of the World Wide Web in the mid-1990s. For example,traditional auction markets have been adapted to the web. Familiarauction types, such as Yankee auctions and Dutch auctions, have beenimplemented effectively by the web-based auction houses (e.g., eBay,Onsale). The once-local classified ad and garage sale are going virtualand global. The very concept of “fixed prices” is under attack, asprices become fluid in the electronic auction environment.

Some economists and even the Chairman of the Federal Reserve Board in1999, Alan Greenspan, have argued that this reduction in market frictioncan be directly translated into the high productivity and low inflationcharacterizing today's economy.

Even though virtual markets are more efficient than traditional markets,they have still not evolved to the point where they can deliver resultsthat even approach the perfect information ideal. Most e-commerce todayis still based on the traditional merchant model in which sellers postfixed offers and buyers visit the sellers searching for the rightproduct. Buyers visit one or more of these sellers to identify who cansupply the needed good or service. Although successful e-commercecompanies (e.g., Amazon) have emerged using this traditional postedoffer, seller-centric business model, the model does not drive any ofthe relevant information costs to near-zero.

More modern forms of markets that are more buyer-centric have also beenemerging. For example, the web has allowed broader application ofbuyer-initiated requests that had previously been restricted toindustrial requests-for-proposals (RFPs) and requests-for-quotes (RFQs).In the mass market space, RFPs and RFQs were found only in selectindustries such as construction. Consumers can now ask for and receiveoffers for a broad variety of commodities such as airline tickets(Travelocity), mortgages (e-loan), insurance (Intuit), automobiles(autobytel) and even house cleaning services (Imandi).

On the web today, independent and non-integrated solutions are beingattempted to address the costs associated with market intelligence(e.g., consumer information, communities), search (directories andguides) and bargaining (auctions). However, if buyers and sellers haveto visit a large number of web sites to accomplish their desired task ofconsummating a purchase optimized to their needs, the problem remainsunsolved—the parties must still search for and integrate diverseresources. This takes time and energy, posing another barrier toefficient transactions. A complete solution should entail not onlyseparate solutions to minimizing costs, it should also integrate thosesolutions within a fast, unified, easily navigated environment.

A final problem for the perfection of markets is the creation of abusiness model to gather and sell information. One limit to acquiringinformation in a bargaining situation is that the parties may want todistort their apparent preferences to improve their bargainingpositions. For example, a buyer may want the seller to believe that itwill not buy unless a price is significantly reduced. Two difficultiesin selling information are that buyers of information may be suspiciousbecause the quality of information cannot be verified before theinformation is transferred; and that it is difficult to prevent highlyvalued information from being resold. In current practice, it is unusualfor retail consumers to pay large sums to acquire market intelligence.Often, they expect sellers to make such information available for free.

The present invention provides a complete solution to minimizing theabove types of transaction costs; unifying the buyer's experience; andcreating a revenue model for the market information that both emergesfrom and drives the solution.

II. The Problems to be Solved

Summarizing the foregoing discussion, today's web-based electroniccommerce market contains two related problems to be solved.

First, there is the problem of minimizing five types of transactioncosts market intelligence cost, search cost, bargaining cost, sellers'transaction execution cost and buyer's integration cost. The goal is toreduce all five costs to near zero.

Second, there is the problem of constructing a business model thatcreates a sustainable revenue source to support the solution to thefirst problem.

(Note: Hereafter, female pronouns are used for the buyer and malepronouns for the seller.)

A. Market Intelligence Cost

Before a buyer converts latent demand into a purchase, she may undergo aprocess of research and education about her needs. Eventually, therelevant decision criteria are settled and the buyer is ready to moveahead. The buyer develops a mental template that identifies the mostimportant benefits and features (and those to avoid). The template iscontinuously refined through both casual and purposeful research untilit stabilizes.

In economic terms, the buyer invests time and energy to create atemplate of decision variables that describe an optimal product thatmatches her goals (utility function). The sellers invests time, energyand money looking for buyers that best match their business goals(production function). The investment by buyers and sellers can be high.

Despite the high value of research to buyers, Internet economics havevalued research at close to zero. Even in markets such as stock trading,where presumably the value of knowledge and information is high, mostfirms find that they cannot successfully charge for informationproducts. The exception is in highly specialized professions, such asjournalism, medicine, law and engineering—but it is a rare phenomenon inthe mass consumer market. Hence, individual buyers are left to their ownrandom devices to gather the information necessary to make a decision.The result is usually wasted time, fatigue, confusion, andfrustration—which can cause the buyer to stop learning.

For sellers, besides learning about the buyer, they must learn enoughabout one another's offers to be able to match or beat theircompetitors. Moreover, that information must be available early enoughin the process for competitors to act upon it, tailoring offers to theindividual buyer in full knowledge of the competition.

There are adverse systemic effects if the sellers do not know whatoffers their competitors are making or which competitors are beingevaluated. A seller who thinks that he may be facing no competition oronly weak competition will not be likely to offer the most favorableprices and terms to the buyer. Perfect information will motivate sellersto bid aggressively and buyers to collect multiple offers, provided thesearch and bargaining costs of doing so are sufficiently low.

B. Search Cost

Once the education phase is concluded and the buyer has developed amental template, a search commences that leads to a transaction.

For buyers, search costs arise primarily from the time it takes tosearch through a bewildering array of commercial sites on the web forthe best possible deal. Evaluating a deal accurately involves accountingfor differences in all the terms, including, without limitation, theprice, product features, brand, delivery time, warranties, shippingcosts, financing, and seller reputation. For sellers, search costsinclude marketing expenses, advertising, direct sales force salaries,and, in general, any costs associated with learning about customerneeds.

Today, a buyer cannot perform a comprehensive search for relevantinformation about the spectrum of seller offers because there are fartoo many electronic merchants. Thus, a buyer will typically visit only ahandful of commercial sites before accepting a (possibly) sub-optimaloffer because she lacks the time to gather information about betteroffers.

The problems with search costs are especially acute for a buyer who isconcerned about more than just price. Each seller that this buyer findsmay offer a different array of options (e.g., product features,warranty, financing terms, delivery time, and shipping and handlingcosts) that require time-consuming evaluation and comparison by thebuyer. Because more time must be spent analyzing each individual offer,this buyer will restrict her attention to even fewer sellers and remainignorant of other, possibly better, offers.

Similarly, a seller may not attract all the buyers he could serve. Forinstance, a seller that emphasizes one product or service feature inadvertising (because marketing data indicate that a majority of buyersvalue that feature) may fail to reach a substantial minority ofcustomers that place a higher value on unadvertised aspects of theproduct or service. It may also happen that the buyer is unaware thatthe advertised product is a close substitute for the product specifiedin the buyer's search.

High buyer search costs not only result in high direct costs to buyers,they can also have harmful systemic consequences. To the extent thathigh search costs keep buyers poorly informed about potential sellers,inefficient sellers with relatively poor products and high prices aremore likely to survive in the marketplace. At the same time, relativelyefficient firms will have more trouble attracting customers and will beslower to grow. In the language of economics, this means that sellerentry and exit decisions will be inefficient.

As a further result, even a buyer who finds searching relativelypainless will have greater difficulty finding the best deal in a sea ofinferior deals, which discourages buyers from searching. Reduced buyersearch activity, in turn, contributes to pricing inefficiency bysellers. Sellers that know that most buyers do not have time to searchfor the best deals are likely to set higher prices for all buyers,because they cannot tell the well-informed and poorly informed buyersapart.

Looking to the future, the problems associated with high search costswill only increase as the scope and diversity of electronic commercecontinue to expand. Because markets function best when information isnear perfect, there is tremendous economic and social value to be addedby an invention that drastically reduces these costs in electroniccommerce.

C. Bargaining Cost

Even if buyers and sellers were able to identify the right partners atzero cost, they would still face the bargaining cost of adjusting theinitially offered terms to serve the parties' mutual interests. Foroptimal bargaining, both the buyer's priorities and the sellers' costsand business objectives must be taken into account, which requires somemechanism for the relevant transaction information to be communicated toeach party and some mechanism for dynamic adjustments by buyers andsellers based on this information. Today, however, there is no practicalway for sellers to adjust their offers quickly and cheaply to each buyerin response to information they have received about that particularbuyer's goals, their competition for that buyer, or their own changingbusiness rules. Similarly, there is no quick and easy way for a buyer tocommunicate to sellers changes in her spectrum of buying criteria for aparticular good or service.

As a result, a seller today can make mistakes because he does not knowthe buying behavior of a particular buyer, the buyer's purchasingcriteria for a particular purchase, or who his competitors are for aparticular buyer. For example, a seller may lose a much-wanted salebecause he did not adjust his offer to meet a particular buyer's needs(because the seller did not know the particular needs of the particularbuyer) or the seller may sell for too low a price because he did notknow the offers being made by his competitors.

There is one exception to this generally negative scenario in presentelectronic markets: sellers learn some of the characteristics of repeatbuyers. Even so, a buyer who wishes to preserve her anonymity cannotcommunicate her particular desires to a large number of merchants inorder to fine tune the bargain. As a result, a buyer may become lockedin to the sellers that know her and pay too much or settle for a lessthan ideal product or service because other sellers have such imperfectinformation.

Today, sellers often lack relevant information about particular buyersbecause the existing mechanisms to provide such information are toocostly, too slow, and too cumbersome to be effective. Yet thisinformation has considerable value to a seller trying to optimize hisoffers. Seller decisions based on direct knowledge of a buyer's needsare superior to those that rely on statistical estimates and conjecturesderived from aggregate marketing data. A well-informed seller couldtailor his decisions to each particular customer if he knew whichfeatures that customer valued.

To eliminate bargaining costs, there need to be mechanisms that enableeach seller to fine-tune his offer to each buyer in multiple dimensions(including price, product features, warranty, financing terms, deliverytime, and shipping and handling). Properly implemented, the mechanismcould further reduce costs by making suggestions to sellers aboutsubstitute, alternative, or complementary products that closely trackthe buyer's preferences. However, if implemented by individualsellers—each in his own way—the very richness of the seller's responsecould also increase buyer search costs by forcing buyers to spend moretime identifying the best deal at each seller's site. Thus, there is aneed for integrated systems and methods that encompass relevant sellersto reduce both search costs and bargaining costs simultaneously.

Besides the mistakes made in individual transactions, a seller'simperfect information about his customers can lead to other mistakes aswell. The seller who never learns why he has lost a sale may fail toadjust his future offerings accordingly. In addition, a seller that hasthe potential to succeed in a market may fail to get the information heneeds to learn to be successful, thereby leading that seller to make anincorrect decision to leave to the market.

From the viewpoint of overall social welfare, these seller mistakes arecostly. Sellers who can most efficiently produce an offer in exactly thedimensions desired by the buyer should get the business, because theymaximize the buyer's satisfaction while economizing on society's limitedresources. In a perfect market, the efficient sellers with respect tothe specific buyer at hand get her business, which is part of what makesperfect markets so desirable.

D. Sellers' Transaction Execution Cost

Electronic commerce had as it roots the Electronic Data Interchangeinitiative, which was largely developed and promoted in the 1980s. EDIis an expensive and cumbersome system of agreements between merchants tostandardize the way they communicate with each other acrossorganizational boundaries.

The World Wide Web has significantly streamlined and improved on EDI.Seller-oriented initiatives such as Extended Markup Language (XML) areunifying the exchange of electronic information between businesses.Without such standards, businesses would be unable to place purchaseorders and fulfill them accurately.

On the web, because buyers and sellers can each be either individuals orbusinesses, an informationally perfect e-commerce environment needs toembrace both the business-to-consumer (B2C) and the business-to-business(B2B) standards.

E. Buyer's Integration Cost

When a buyer has to deal with multiple sites to consummate atransaction—each with its own user interface and methods—the experiencecan be tedious. A complete solution from the buyer's perspective shouldminimize all types of costs within one integrated environment, withoutthe need to visit a number of different sites and to re-enter data ateach new site. Today, however, a user who wishes to get marketintelligence, conduct an exhaustive search, bargain to a successfuloutcome and effect a transaction would have to visit dozens, perhapshundreds of web sites.

A common buyer response to this integration cost is to concede defeat atthe outset and not shop aggressively. Recent studies indicate thatnearly one third of all buyers who decide not to buy are deterred by thehigh cost of learning yet another user interface and entering morepersonal data.

Modern e-commerce sites offer convenient methods for executingtransactions, e.g., Amazon's “one-click.” The industry is beginning todevelop standards to help minimize the overhead (time and energy)involved in consummating transactions.

Buyer-oriented initiatives such as Open Profiling Standards (OPS) andthe E-Commerce Markup Language (ECML) are attempts to create anelectronic wallet so that the buyer does not have to repeat tediouslythe entry of her name, address, credit card information and similarconventional data.

Despite these efforts, none of the prior solutions has attempted tofully integrate the entire process into one seamless experience.Instead, as described below, today's solutions remain fragmented.

III. The Limitations of Existing Solutions

There has been progress made with respect to some of the cost categorieson a piecemeal basis, using a variety of different approaches, but noneof the web sites that exist today makes a systematic and integratedattempt to eliminate or reduce all five kinds of costs (i.e., marketintelligence, search, bargaining, sellers' transaction execution andbuyer's integration costs).

A. Solutions that do not Create New Markets

1. Search Engines, Directories, Guides & Portals

One early attack on the problem of information costs was the searchengine, which, as its name suggests, aims to reduce search costs. Asearch engine is a remotely accessible computer program that lets peopledo keyword searches for information on the Internet. For electroniccommerce applications, typing a keyword or phrase that describes thedesired good or service can launch a search. Search engines, however,have a number of shortcomings in providing a solution to the problemsdescribed above. In particular, buyers often find that the searchresults are not relevant, and visiting each web site suggested by asearch engine can be a laborious and fruitless task. A search onAltaVista reveals that there are over 1,500 search engines in operation,ranging from the most comprehensive (AltaVista) to highly fragmented andspecialized engines.

Several solutions have emerged to address the problem of low relevancy.Some search engines evolved into e-commerce search guides. There arehundreds of e-commerce guides on the web today. Some of these aregeneral guides, but most specialize in a single area of e-commerce.

Guides and directories, such as LookSmart, GoTo.com, HotBot and Google,and meta-search engines that can search multiple search engines with onequery, such as AskJeeves, are examples. These cover only a subset of theweb and employ humans to physically check on an included web site toensure its relevance and content. In principle, guides are distinguishedfrom directories by their inclusion of additional editorial content,though the line between the two can be fuzzy.

The limitation of these sites as e-commerce entities is clear to anyuser: relevance and coverage remain unsolved problems. Usually, afterthe search is conducted, the user is essentially transported to anotherweb site to do the shopping.

Moreover, even if this problem were solved, these sites still deal onlywith a single cost category—search costs—and even this is only addressedin a partial way.

2. Electronic Superstores and Transaction Aggregators

The superstore phenomenon on the web is a natural extension of the realworld parallels, e.g., WalMart, Macy's and Price Club. These retailentities aggregate products and services from a wide array of vendorsand present them to buyers in a consistent environment, namely thestore. Physical superstores reduce search costs and travel costs byproviding a single place where a buyer can find much of what she wantsin a single trip.

Similarly, numerous web-based superstores have emerged. Most superstoresare little more than large aggregations of e-sellers. More modernsuperstores add value by creating a unified answer to a buyer's search—displaying results from a number of e-merchants in a consistentfashion.

In competition with the superstores, many search engines have evolvedinto portals, rich in content, organized like a communication orentertainment network. In most portals, humans maintain guides to theweb, thereby increasing the relevance of search results obtained usingthose guides. The trend is for a portal to create a network ofaffiliated e-merchants and thereby begin to act as a transactionaggregator, which is just a variation of the superstore concept.E-commerce has become such a critical part of the portal business modelthat every major portal has developed its own shopping channel, e.g.,AOL Shopping, Yahoo! Shopfind, Excite Shopping and AltaVista Shopping.

In the current design of all significant superstores, when a buyer runsa search, similar products from various e-sellers are listed in acoherent manner that promotes quick comparison—at least of price.However, to effect the transaction, a buyer is still handed off to ane-seller so integration costs remain high.

The portal-hosted shopping channels organize a relatively small group ofsellers and are thus able to create a unified look-and-feel and allowcomparison shopping within their limited universe. An importantlimitation to such sponsored sites is the buyer's awareness that onlye-merchants in the superstore are affiliates of the host. This issimilar to seeing a tourist guide in a hotel featuring the bestrestaurants in town, only to realize that those same restaurants paid tobe featured. Another important limitation of these sites is that theprice and other terms of the transaction are fixed. There is notailoring of the transaction to meet the needs of an individual buyer,so bargaining costs remain high for the buyer.

3. Shopping Comparison Engines & Bots

Price comparison engines are limited because they only cover a smallsubset of the web.

Product and vendor searching is the stage of e-commerce where softwareagents have classically been deployed in retail electronic commerce. Thegoal is to find items that the user wants to buy. This involvesidentifying appropriate vendors, comparing their products, etc. Earlysearch agents such as BargainFinder (from Andersen Consulting) lookedfor a specific product and compared vendors based on price alone.BargainBot and Fido extended this by allowing rough matches of theproduct name. In the future, comparisons will most likely be conductedbased on user preferences defined on a larger set of features, includingprice, product attributes, delivery characteristics, details offinancing offered, etc. Jango (from NetBot, bought by Excite) andAdHound already allow the user to specify the product by features, butthey do not support tradeoffs among features. An example of such atradeoff would be if a consumer could accept a longer delivery time fora lower price. Allowing the user to express tradeoffs enables the systemto find the offerings that best suit the user's need. In other words, itallows the offerings to be ranked based on how desirable they are to theuser. Furthermore, communicating this information on tradeoffs to thesellers allows them to redesign their offerings so as to create tradesthat are more desirable for both parties.

Shopping bots are a newer class of solutions that actually delegate thesearch and bargaining to a software process. A growing body of academicliterature is focusing on e-commerce agents that can act autonomously onbehalf of sellers and buyers. These software processes are not in commonuse because consumers are not yet willing to entrust their decisions toan automatic process.

In principle, shopping bots can reduce search costs, but they are oflittle use in reducing the costs in other categories. The buyer muststill educate herself prior to giving instructions to the shopping bot.The bargaining costs in a negotiation between a buyer (bot) and a seller(bot) are high because the parties have incentives to misrepresent theirtrue preferences in an attempt to get a better deal. In contrast, in abuyer's auction with near perfect information, the buyer has a strongincentive to reveal her desires accurately and the sellers are motivatedto fulfill those desires as completely as possible in a cost-effectiveway. Finally, the integration costs for the buyer remain high becauseshopping bots only address a small piece of the overall problem.

B. Markets without Dynamic Offer Adjustments

According to neoclassical economic theory, it is possible in principleto create a perfect market even with simple posted prices, in whichoffers are not adjusted dynamically. In that theory, each seller simplyposts a fixed price for every variety of every good that he offers.Then, if the prices are market-clearing ones, which means simply thatsupply equals demand for each good, the outcome is Pareto efficient. Inthis theory, nothing can be gained from the ability to adjust offersindividually to each potential customer.

Neoclassical theory had its origins as a description of commoditymarkets. In those markets, the goods being traded were relatively few innumber, so that the same good would be purchased by many buyers. Inmodern e-commerce, by contrast, it is entirely possible for eachcustomer to buy a different good, distinguished by product features,warranty, delivery terms, date, location, and so on. Indeed, there aremany possible specifications that will not be purchased by any customer.For such goods, it is prohibitively expensive to list all possiblevarieties and their prices in advance and there is no experience whichpermits the parties to know the market-clearing prices. These two factsgreatly limit the practical usefulness of neoclassical markets withoutdynamic adjustments in an economy where flexible specialization in manyfirms can provide a wide variety of physical products on a wide varietyof terms.

Within the category of markets without dynamic offer adjustments, onecan distinguish four types, which are tabulated and discussed below.

TABLE 1 Markets Without Dynamic Offer Adjustments One Seller ManySellers One Buyer 1. Private sale without 4. Fixed Price RFP/RFQnegotiation Many Buyers 2. Posted offers 3. Matching services

1. Private Sale without Negotiation (One Buyer, One Seller)

Private sales arise when the market intelligence and search costs are solarge relative to the value of the transaction as to make informationgathering and exchange uneconomic. In principle, they could also arisewhen there actually is only one potential buyer and seller and theiridentities are already known. Even in these situations, bargaining wouldnormally ensue unless bargaining costs, too, are high. This unusualsituation is one to which the present invention does not apply.

2. Posted Offers (Many Buyers, One Seller)

This is the most common form of an e-commerce market without dynamicoffer adjustments. There are literally hundreds of thousands ofe-commerce entities that serve as electronic intermediaries betweenbuyer and seller. Their whole purpose for existing is to capture thevalue of reduced costs of running a market.

New types of electronic infomediaries also include virtual superstoresand cybermalls. Electronic superstores typically specialize in just oneproduct category, such as books, travel, or cars. Cybermalls serve astransaction aggregators that allow a buyer to interact with a number ofprimary providers of goods and services.

However, all of these electronic commerce web sites deal with onlypieces of the problem. The e-commerce entities described in Table 1 allconduct business without dynamic offer adjustments. They cannot, even inprinciple, offer a comprehensive solution to eliminating search andbargaining costs and facilitating competitive intelligence, for severalreasons.

First, prices and terms for goods and services at these sites are postedand fixed. This affords no opportunity for the parties to tailor anagreement that serves the mutual interests of the buyer and seller.Sellers cannot dynamically adjust their offers to match individual buyerneeds. Bargaining costs thus remain undiminished.

Second, the selection is limited to the goods and services offered bythe seller(s) at that portal, superstore, or cybermall. Buyers muststill make decisions based on imperfect information, i.e., there may beanother portal, superstore, or cybermall with a better total offer.There is no way for buyers to compare offers with differing terms acrossdifferent commercial sites. As the number of these sites grows, thetransaction costs will prohibit buyers from gathering all the relevantinformation about the sellers at each of these sites via existing searchmethods. Search costs thus remain too high and bargaining costs are notreduced.

Third, sellers are forced to make decisions based on imperfectinformation about the buyer and the competition, because these sitesprovide little or no information to the seller about the buyer'spriorities or the seller's competition for a particular buyer at thatinstant in time. Competitive intelligence is not improved.

There are a number of existing mechanisms that sellers can use to gatherinformation about their customers and their competition. These includehiring market research companies, subscribing to database publishers,using computer programs to monitor electronic markets, attending tradeshows, and surveying their customers. These methods, however, are slow,expensive, and provide only aggregated information. They do not provideinformation about an individual buyer's goals or the seller'scompetition for a particular transaction. Thus, sellers today cannotdynamically adjust their offers for each buyer based on relevant tradeinformation because there is no method available to gather thisinformation in real time at low cost.

Market mechanisms based on fixed, posted prices are inherently limitedin their ability to resolve the search, bargaining, and competitiveintelligence problems. When offer adjustments are made at these sites,it is usually a global price change that applies to all customers. Atpresent, these sites cannot tailor their offers to individual buyersbecause this would require using a direct sales force (i.e., humanintervention), a method that is prohibitively expensive for mostautomated, web-based businesses.

3. Matching Services (Many Buyers, Many Sellers)

The classic rationale for monetizing a market (i.e., prices) is toreduce the cost of matching buyers and sellers. For markets that areinherently non-monetized, such as housing agencies, dating services andemployment agencies, the role of an agent is critically important. Theagent acts as infomediary.

Computers are particularly adept at matching. A computer can process anunlimited number of variables and find the best match for a specificindividual. The rapid growth of web-based employment agencies shows thateven for “high touch” services, the web is an effective tool forreducing the cost of matching. Current examples include successful websites for jobs, dating services and apartment hunting. In the future,matching services will become even more specialized.

Although matching services do lower search costs, they do not addressthe other components of the problem. A person still has to educateherself about what type of match she is looking for, e.g., given thebroad spectrum of possible jobs, which types of jobs is she interestedin matching. Matching services do not provide any form of dynamic offeradjustment. They simply bring two individuals together. The rest is aone-on-one negotiation, so bargaining costs remain high. Integrationcosts also remain high because matching services only address one pieceof the overall problem, i.e., search costs.

4. Fixed price RFPs & RFQs (one buyer, many sellers)

The oldest type of buyer's market is the Request for Proposal (RFP) orRequest for Quote (RFQ) process, which predates the Internet. Thisprocess is used by large institutions such as government agencies andcorporations that need to make high-value, complex purchases and canafford the transaction costs. Here, a large government or corporatebuyer typically spends weeks or months developing a formal request for aproposal, which it then sends out to potential sellers. In many cases,the RFQ is only submitted to a carefully pre-qualified group of sellers.Interested sellers then spend weeks or months developing formalproposals, which are submitted to the buyer as sealed bids. The buyerthen chooses the bid that most closely matches her needs. Needless tosay, the RFP/RFQ process has very high transaction costs (both time andmoney) and often involves imperfect information between sellers (e.g.,the sellers do not know each other's identities or bids).

The practical significance of the high transaction costs is reflected inthe common auction features that aim to reduce these costs. One suchfeature is the pre-qualification and short-listing of bidders. Becausepreparing bids for an auction can be expensive, bidders want to beassured that they are not wasting their money preparing bids when theyhave no chance of winning To the extent that evaluating bids isexpensive, the buyer, too, may wish to limit bidders to those itbelieves can deliver on its promises.

Other features that signal high transactions costs are that the buyersin many auctions present detailed product specifications in advance orcommit themselves to purchase from some bidder. These practices, too,help to protect the bidders from spending money on a fruitless cause. Inthe absence of significant transactions costs in the auction, one wouldnever see pre-qualification of bidders, detailed prior specifications,or commitments by buyers.

Prior to the Internet, because of these high costs, RFPs and RFQs wereprimarily used for business-to-business trade. It was impractical for anindividual buyer to issue personal RFPs or RFQs. The time and money thata buyer would spend contacting an indefinite number of potential sellerswould far outweigh any benefit from doing so (e.g., getting better termsand conditions) for all but the largest purchases. The Internet,however, has enabled new types of buyer's markets in various industries.They all share common characteristics: the buyer creates a request byusing a template provided by the infomediary or e-seller. The templateis transmitted to the relevant e-sellers (unless the seller is theprimary producer). The sellers respond to the buyer with a quotation.Often this process is conducted by humans and a negotiation follows inthe traditional style.

There are many examples of consumer RFPs and RFQs conducted on theInternet, e.g., consumer RFQs for automobiles, air travel, mortgages andeven household services. All of them have a fixed offer price—theconsumer requests a quote, and the seller provides a posted offer.

These services are not designed to offer dynamic pricing. Hence, theircentral value is quickly matching buyers and sellers—a reduction in thesearch cost but not a reduction of bargaining cost. In addition, simpleon-line RFPs and RFQs do not lower either market intelligence orintegration costs. The buyer must educate herself beforehand and put thevarious pieces of the transaction together by herself.

C. Markets with Dynamic Offer Adjustments

To facilitate personalized, customized exchange, parties need to be ableto adjust their offers dynamically. Auctions, which have existed forcenturies, are the traditional market solution for creating dynamicoffers. A series of electronic solutions—supporting either bargaining orauctions—has evolved to enable this type of market.

In contrast with the “posted price” web sites, an auction has no fixedposted offer (other than the reserve price). Sites that support dynamicoffer adjustments can be divided into four main categories, discussedbelow. The present invention resides in one of the four spacesidentified in the typology below —one buyer, many sellers (categorynumber 4).

TABLE 2 Markets With Dynamic Offer Adjustments One Seller Many SellersOne Buyer 1. Negotiation 4. Buyer's Auction classified ads includes thepresent invention Many Buyers 2. Seller's Auction 3. Exchange Auctione.g., traditional Yankee e.g., stock & commodity and Dutch auctionstrading

1. Negotiation (One Buyer, One Seller)

This type of market is a negotiation, not an auction. They can be twoparty or multilateral (e.g. parallel one-on-one negotiations). It isincluded here for completeness.

Negotiations do not address market intelligence, search, sellerexecution or integration costs at all. Negotiations just adjust theelements of an ask/bid. However, as noted previously, the bargainingcosts in a negotiation between a buyer and a seller are high because theparties have incentives to misrepresent their true preferences in anattempt to get a better deal. In contrast, in a buyer's auction withnear perfect information, the buyer has a strong incentive to reveal herdesires accurately and the sellers are motivated to fulfill thosedesires as completely as possible in a cost-effective way.

a) MIT Media Lab—AMEC—Frictionless—Tête-à-Tête

MIT Media Lab's Agent-Mediated Electronic Commerce project (AMEC) hasproduced a collection of companies, projects and research papers, mostlyfocused on collaborative filtering (Firefly), multi-dimensionalevaluation and recommendation engines (Frictionless, Tête-à-Tête) andautonomous bots (AMEC). The latter involves autonomous software agents,“bots,” that are focused on one-one-one negotiations with and withouttheir owner's intervention. The academic question is whether such botscan be trusted and manipulated (corrupted) and whether theirnegotiations produce optimal results for both buyers and sellers.

In the Kasbah system (Robert Guttman, Pattie Maes et al. at MIT MediaLab), non-mobile software agents negotiate over price, conceding overtime. The user is allowed to choose between three concession rates, butthere is no justification why a rational agent would use thoseparticular strategies.

In the Tête-à-Tête system (Robert Guttman, Pattie Maes et al. at MITMedia Lab; being commercialized by Frictionless Commerce), each user canspecify preferences and a matching engine tries to satisfy both parties.Each user's utility function is assumed to have a particularparameterized form, and the user gets to adjust the parameters. Theauthors claim that the system does product brokering, merchantbrokering, and negotiation, but Tête-à-Tête does not address the marketintelligence, seller execution, or integration aspects of the overallproblem.

The present invention is not concerned with autonomous negotiating botsand therefore the MIT-related work is interesting but not directlyrelevant.

2. Seller's Auction (Many Buyers, One Seller)

Physical seller's auctions are familiar—many people have seen orparticipated in a Yankee auction, whether at the county fair or at anart gallery. Their electronic counterparts have emerged as a successfulform of e-commerce.

There are hundreds of seller's auction sites operating on the web today.The reason for their success is that they take advantage of the virtualnature of markets on the web. Auctions dispense with fixed prices—andboth buyers and sellers seem to like it. Sellers find it easy to set upseller's auctions for new goods, remainders, used goods and services.Buyers find it both convenient and entertaining to engage in a biddingcontest for goods and services. For every buyer suffering from winner'scurse (overvaluing a product), there is a success story of someonewinning an incredible bargain.

Just as buyer's auctions can reduce transaction costs when buyer wantsare unique and sellers can adapt to those, seller's auctions can reducetransaction costs when the items being sold are fixed in character andbuyers need to investigate and evaluate each item separately. Asdiscussed below, however, seller's auctions generally do not minimizethe transaction costs described above.

a) eBay and Related Examples

There are numerous variants to the seller's auction, each with its ownset of auction rules. These auctions include, without limitation, aYankee auction, a silent auction, a sealed bid auction, and a Dutchauction. The most common type of multiple buyer-one seller auction isthe Yankee auction, in which buyers compete with other buyers byincreasing their bid prices, all the buyers get to see each other'sbids, and the highest bidder gets the good or service being offered bythe seller. Web sites that currently use a Yankee auction include eBay,Onsale, and Bid.com.

Although these auction sites do enable real-time adjustment of prices,they do little to alleviate the central problem of reducing the buyer'stransaction costs. A buyer must still go through the tedious,time-consuming process of finding auctions that have a particularproduct or service that the buyer may want. The buyer must then comparethe product or service attributes (other than price) being offered ateach auction site to decide which product or service at which site sheshould bid on. If several products appear satisfactory, the buyer musteither limit herself to bidding on a single one, hoping that the pricewill be acceptable, or must track several auctions simultaneously to bidin each. In an active auction for a single item, most bidders have theirbids rejected and are left to continue their search, adding to buyersearch costs. Even the “winner” may end up buying a product or serviceat one site when another site had a different product or service thatthe buyer would have preferred at its closing price.

In addition, the only aspect of the transaction that is adjusted inthese auctions is the price—all other terms are fixed. Thus, the buyerand seller may end up exchanging the product or service on sub-optimalterms.

Moreover, many buyers would find it too costly to participateeffectively in a seller's auction, because effective participationrequires accurate information about what the item should cost, which inturn depends on detailed knowledge of the whole product category. Forexample, an auction of a stereo may require not only that the buyer befamiliar with the characteristics of the brand being offered but alsohow those characteristics compare to other available brands and whatthose other brands cost. Such buyers would not want to compete withother buyers for a particular good or service. Instead, these buyerswould prefer to simply send out a request for an offer and then let thesellers compete with each other for the buyer's business. Similarly,seller's auctions do not meet the needs of sellers who want to be ableto adjust their terms to create the most attractive package for theparticular set of potential buyers in the auction.

b) Mercata, Accompany

The definition of “buyer” is changing because of the web. In the past, abuyer has been assumed to be either a person or a company. Morerecently, a buyer could be construed as a software agent (a bot). Alsorecently, the web has enabled the ability to create buying groups thatact as a single entity. The groups combine their purchasing power andcan thus cause sellers to lower their prices. Mercata and Accompany arebuying groups with dynamic price adjustment. These systems share fewcharacteristics with the present invention and have a number oflimitations and shortcomings in providing a solution to the problemsdescribed above. These systems do not address market intelligence,search, or integration costs at all. Moreover, the systems are notnecessarily auctions—it is possible for a seller to simply post an offerwith a downward-adjusting price as more buyers sign on. In some ways,this is an automated form of a Dutch Auction. The systems adjust onlyprice, not other terms. They do not offer a recommendation or evaluation(largely because it is a single-product auction, not one with choice).They, by definition, do not operate in real time—the auction is heldopen waiting for additional buyers to sign on and thus reduce the pricefor everyone.

3. Exchange Auctions (Many Buyers, Many Sellers)

Electronic stock and commodity exchanges are auctions with multiplebuyers posting binding “bid” prices and multiple sellers posting binding“ask” prices. The most well known exchange of this type is NASDAQ.Access to NASDAQ is achieved via a number of successful on-line tradingsites, e.g., Schwab, e*trade, DLJ and Ameritrade.

In exchange markets, buyers compete with each other to meet their “bids”and sellers compete with each other to secure their “asks.” A buyer atone moment might become a seller at the next moment. This market hasreal-time adjustment of prices, with both buyers and sellers havingnear-perfect information about all prices and quantities.

These auctions also have important limitations. An inherent feature oftheir design is that the only aspects of the transaction that can beadjusted are quantity and price—all other terms are fixed. This maysuffice for commodity goods and financial securities, but it can resultin sub-optimal transactions for more complex goods and services.Moreover, because all the goods exchanged in the auction have the sameprice, these auctions cannot be used to sell non-identical goods.Consequently, they do not reduce the search costs of a consumer whowishes to compare these goods against similar but not identical goods.

Moreover, for the reasons noted above, many buyers do not want tocompete with other buyers for a particular good or service. Instead,they want to simply send out a request for an offer and then let thesellers compete with each other for the buyer's business. Similarly,many sellers do not want to compete with each other in the presence ofmultiple buyers, but would prefer to tailor their offer to a singlebuyer whose multi-dimensional needs are explicitly understood by thesellers.

4. Buyer's Auction (One Buyer, Many Sellers)

The buyer's auction is the space in which the present invention resides.The Internet has enabled so-called “buyer's auctions” to be practicalfor a far wider range of goods and services than was previouslypossible. In these auctions, the buyer is the center of attention, withmultiple sellers vying with each other for the buyer's business. Many ofthe existing buyer's auctions reduce the cost of transacting some typesof business, but most do not eliminate or reduce the five transactioncosts discussed previously.

There are several current examples of buyer's auctions that illuminatethe limitations of today's approach and the new role for the presentinvention. Each example is described briefly below and importantfeatures are identified that distinguish it from the present invention.All of the current buyer's auctions are flawed in one or moresignificant respects, which prevents them from being full solutions tothe problems described above. Moreover, none of the current buyer'sauctions teaches or suggests the business methodology (revenue model) ofthe present invention for gathering and selling the marketinginformation generated by a buyer's auction with near perfectinformation.

It will be demonstrated in the next section that the present inventionis a pure buyer's auction, differentiated from all previous attempts atcreating such a market.

a) FreeMarkets

FreeMarkets conducts business-to-business auctions that enable largebuying organizations to purchase industrial materials and components.The FreeMarkets process is similar in most respects to the traditionalRFQ process described above, with the exception that all bidding takesplace online.

The FreeMarkets process is time- and labor-intensive. A team ofFreeMarkets employees is assigned to each project. The team spends weeksworking with the buyer to develop a comprehensive RFQ with detailedtechnical, commercial, logistical, and quality specifications of thesupplies to be purchased, so that only price adjustments can be madeduring the auction itself. The RFQ is distributed a few days to severalweeks prior to the auction. Only pre-screened suppliers invited by thebuyer can participate. As previously observed, this is itself a signalthat the transaction costs of the auction are quite high.

During the auction, which takes place online, sellers can place bids for“lots” of supplies that they want to sell to the buyer. “Lots” are partsor line items that have been grouped together by the buyer. The sellerscannot place bids to sell individual parts or line items to the buyer.At the same time, the buyer is committed to awarding all line itemswithin a lot together. Moreover, the buyer must commit to making anaward to at least one seller at the beginning of the FreeMarketsprocess. The sellers remain anonymous to other sellers during theauction, but they can see the evolving market price for a lot, and theycan respond with new bids. The only variable in the auction is the lotprice. All other variables are fixed by the RFQ. A typical biddingsession lasts two to four hours.

After the auction, sellers with competitive bids must go through furtherqualification steps, including submitting cost breakdowns to the buyerand supporting on-site visits by the buyer. Thus, the buyer can onlyconsider non-price factors outside the auction itself.

FreeMarkets is a buyer's auction; however, the FreeMarkets solutiondiffers significantly from the present invention and has severallimitations and shortcomings in providing a solution to the problemsdescribed above. It has high transaction costs—both time andlabor—before, during, and after the auction, which would be intolerablein many business-to-business, business-to-consumer, andindividual-to-individual transactions. The buyer must approve thesellers beforehand, which is time consuming and can result in the buyerexcluding a seller who unbeknownst to the buyer could have provided thebest offer. The auction itself takes several hours and there are severaladditional time-consuming steps that the seller must go through prior tocompleting the transaction (e.g., hosting site visits by the buyer).

The only variable that changes during the auction is the price. Also,the buyer cannot change her mind and withdraw the RFP—the buyer iscontractually committed to buy from at least one of the sellers from theoutset as an inducement for sellers to expend the resources necessary toenter a proposal. These features are, as previously observed,indications of the high costs of the bidding process. Theses featuresalso make the process inappropriate for inexperienced consumers who maywant to see an offer before deciding whether or what to buy. In terms ofthe five costs discussed above, the process entails significant costsfor the buyer in every category.

The sellers are also burdened with imperfect information. Although theydo see the lowest price offered for a lot, they do not know theidentities of the competing sellers. This lack of knowledge may lead aseller to make a sub-optimal bid. For example, a seller with anexcellent reputation for service and support may erroneously decide tomatch the low bid of a competing seller with a poor reputation, therebygiving up the premium that he could have captured for himself because ofhis superior reputation (i.e., brand power). Moreover, the buyer canonly consider important factors other than price outside the auctionitself—for example, vendor-supplied financing, warranty, and all theother terms and conditions associated with complex purchases.

b) Orb|Bid

Orbbid.com is a web site that describes an RFP/RFQ concept that focuseson multidimensional RFPs and RFQs, facilitates multidimensional offersby human sellers and includes recommendations and evaluations usinghuman experts.

Orb|Bid can conceptually operate as a buyer's auction; however, theOrb|Bid process differs significantly from the present invention and hasnumerous limitations and shortcomings in providing a solution to theproblems described above. It does not address the buyer's marketintelligence costs at all. As in a traditional RFP process, the buyerhas to educate herself. Search costs also remain high. The Orb|Bidprocess does not automatically search for potential sellers. Instead,just like a traditional RFP, the buyer spends time and money topre-qualify sellers or, alternatively, the buyer merely posts the RFP onher own web site and hopes that potential sellers come to her site andfind it. Bargaining costs are higher in Orb|Bid than in the presentinvention's fully automated buyer's auction because of the high costs ofhuman intervention in the Orb|Bid process. Seller offers are adjustedmanually in Orb|Bid during the auction. Similarly, offers are evaluatedby human experts during the auction. In addition to increasing thetransaction costs, the intervention of human experts limits thepractical size of the auction and means that the auction has to bescheduled in advance when the relevant experts are available. As withFreeMarkets, a system designed for industrial (large-scale, high-value,complex) RFPs and RFQs differs significantly from a system geared moretowards consumer-oriented products and services.

c) Priceline

U.S. Pat. No. 5,794,207 describes a type of one buyer-multiple sellersprocess used by Priceline.com. In the Priceline process, the buyersubmits a purchase offer coupled with a payment guarantee (e.g., acredit card account number) to an electronic intermediary. The buyer'soffer is then communicated to a plurality of sellers. The first sellerto accept the offer forms a binding contract with the buyer, therebyending the process. Note that the Priceline process is not an auction intraditional economic terms because one requirement for an auction is acomparison of offers, with inferior offers rejected in favor of superioroffers.

The Priceline approach has numerous limitations and shortcomings inproviding a solution to the problems described above. Marketintelligence and integration costs are not addressed at all. Moreover,there is no auction adjustment of sellers' offers. Indeed, from thebuyer's point of view, there are no seller offers at all—just acceptanceor rejection of the buyer's offer. From the seller's point of view, theauction is not iterative: it makes a single decision to accept or rejecta proposed price (although seller counteroffers are possible). Plainly,bargaining costs are not reduced at all in this mechanism because thereis no comparison of sellers' bids.

Priceline makes no attempt to create near-perfect information betweenbuyers and sellers. The sellers do not have any information aboutcompeting sellers because the process uses sealed bids with no iterationin the bidding process. With no comparison of bids, it would be purecoincidence that the seller that accepts the buyer's offer is the sellerwho is willing to offer the lowest price and best product or service.

In Priceline, the buyer is blind to all sellers' offers except one.Moreover, the buyer does not have any information about the sellersprior to being bound to a contract with a particular seller. Thisprocess can lead to a sub-optimal decision by the buyer in several ways.For instance, the buyer may specify too high a price in her offerbecause she does not know the sellers' rock-bottom prices.Alternatively, the buyer may settle for lower quality service becauseshe could not evaluate competing multi-dimensional offers (e.g., a buyerpurchasing airline tickets may agree to more or longer stopovers atairports). In addition, the seller that accepts the buyer's offer mayturn out to be a business with poor quality or service that the buyerwould not knowingly choose if she had a revocable choice.

Unlike the present invention, the Priceline market forces buyers toaccept any qualified offer. The Priceline market does operate innear-real time, but the buyer has to wait up to one hour for quotationand cannot reenter a new offer if her first offer is rejected. Pricelinedoes not provide a recommendation to buyers or information about themarket or the buyer's characteristics to sellers.

d) Travelbids

Travelbids.com is a web site that conducts two different types ofbuyer's auctions related to the travel industry, which Travelbids calls“Regular Listings” and “Full Service Listings.”

For Travelbids' “Regular Listings,” the buyer does all of the marketintelligence regarding a trip by herself. She also does all of thesearching for an airline (or airlines) that will take her where shewants to go. She makes all the reservations with the airline(s) and getsthe airlines' posted prices, but she does not purchase the ticketsdirectly from the airline. Instead, she lists the reservation withTravelbids. Travel agents then bid to sell the tickets to the buyer in abuyer's auction. The travel agents can see the bid amounts during theauction (but not the bidders' identities). The travel agent bidding thehighest rebate (discount) of his commission takes over the reservation,charges the buyer's credit card, and sends the tickets to the buyer. Thebuyer must accept the winning bid.

Although Regular Listing auctions are beneficial to the narrow marketniche that they serve, they have numerous limitations and shortcomingsin providing a solution to the problems described above. Marketintelligence, search and integration costs are not addressed at all. Thebuyer has to do all this work herself (which is one reason why travelagents are willing to rebate some of their commission to her). Moreover,the only variable that can be adjusted during the auction is thediscount price. All other terms are fixed.

For Travelbids' “Full Service Listings,” the buyer lists herspecifications for the proposed trip, and a maximum of three travelagents can e-mail their proposals to the buyer. The buyer is notobligated to buy any of the three proposed trips.

Like Regular Listing auctions, Full Service Listing auctions havenumerous limitations and shortcomings in providing a solution to theproblems described above. Market intelligence and integration costs arenot addressed. Sellers are blind to each others' offers, the number ofsellers is limited to three, and there is no iteration in the bidding.Thus, bargaining costs are not reduced as much as they would be ifsellers had complete information about each other's bids, if more thanthree sellers were allowed to bid, and if sellers could adjust theirinitial offers.

IV. The Solution: A Buyer's Auction with Near Perfect Information

The present invention is focused on solving the problems and limitationsdiscussed above. From an architectural perspective, the invention isreferred to as a “system,” whereas from a service perspective it isreferred to as an “Auctioneer.” The detailed methodology foraccomplishing these tasks is described below (see the DetailedDescription of the Invention), but its primary attributes include thefollowing:

a) The system implements a buyer's auction that is fundamentallydesigned to minimize market intelligence, search, bargaining andtransaction execution costs and thus create more competitive,frictionless markets.

b) Buyers and sellers can efficiently conduct the buyer's auction withinthe Auctioneer's unified environment, thus minimizing buyer integrationcosts.

c) The buyer's auction generates valuable proprietary information forboth buyers and sellers and a revenue stream (hence a robust businessmodel) for the Auctioneer offering such a service.

A. Nine Key Discriminators

The present invention can be described in terms of nine attributes that,in various combinations, distinguish it from the prior art.

1. The buyer requests the offer.

The buyer initiates the process by requesting an offer. In a preferredembodiment, participation in the auction can be free for both buyers andsellers. This encourages the maximum number of buyers and sellers toparticipate, thereby creating the greatest numbers of RFOs and actualoffers.

One object of the present invention is to perfect markets for consumergoods and services. The products and/or services that consumers want tobuy vary over time, with many items (particularly durables) purchasedonly infrequently. Sellers of these goods and services, however, tend tohave a lasting presence in the market that does not vary greatly fromday to day, which makes it relatively inexpensive for sellers to set upsites to present their goods and services and to use seller bots tonegotiate with potential buyers. Thus, transaction costs for ordinaryconsumer transactions are lowest when the sellers are the ones whomaintain stores or websites and make the initial offers to buyers inresponse to the buyer's stated criteria.

2. There is an auction adjustment of sellers' offers.

Because different buyers have different priorities and preferences, thesellers' offers in a perfect market need to respect that and be tailoredto the particular buyer. To economize on market intelligence, sellersalso need to be made aware of existing competition before proceeding.Without auction-style adjustment, offers could not be tailored soaccurately to the buyer's individual preferences and to the competitivesituation.

3. The auction process is fully automated.

The auction process is fully automated, including buyer's rules andsellers' rules. This design rule is important because employing softwareprocesses (bots) to conduct at least a portion of the auction reducessome of the transaction costs, including time costs. Without thisfeature, limiting auction costs would require employing the kinds of thecost limiting devices used in traditional auctions, such as forcingbuyers to commit, pre-qualifying sellers, limiting the number of rounds,and so on.

In mass market oriented systems, any human intervention imposes laborcosts that can easily make the business model infeasible. Hence, thesystem has to be designed from the ground up without human interventionby sellers and minimum human intervention by buyers. On the other hand,people today do not trust automated processes (bots) to performdecisions for them. Buyers, for example, are unwilling to delegate thebargaining tasks to a bot. The single exception found in consumermarkets is programmed trading. The present invention is designed tosupport the critical aspects of the buyer's auction that are appropriatefor automation, including market intelligence, search and sellers'adjusted offers.

4. The Offer Adjustment can be Iterative.

The auction can be set to have one or multiple rounds with adjustmentfor length of time. However, in the preferred implementation, anopen-ended iterative procedure is used in which bidders must improvetheir offers at each round or cease bidding.

The reason for using an open-ended procedure is that efficient outcomesare reliably achieved only in markets in which the parties have anopportunity to make offers that overturn any inefficient outcome. Thathappens most reliably in an iterative process, in which sellers learnabout the best standing bid and have an opportunity to respond. (Thereare also auction designs, such as the Vickrey auction, that mimic theoutcome of an iterative process in a single stage process. The iterativeadjustment is therefore not a necessary feature of the process.)

5. Both Price and Other Decision Factors May be Adjusted in the Bidding.

The request for offers (buyer's RFO) and answers (sellers' adjustedoffers) are matched in multiple dimensions, agreed upon in advance viaan electronic template so as to minimize market intelligence, search andbargaining costs. If a seller were limited to adjusting his price, theopportunity to tailor the deal to the customer would be lost and theproblem of bargaining costs would not be overcome.

6. There is Near-Perfect Information Available to Both Buyers andSellers.

In a preferred embodiment, the buyer sees all the sellers' offers andthe sellers see both information about the buyer preferences and all theinformation about each others' offers. Without near-perfect information,all the anomalies and sub-optimal results seen in the physical worldwould reappear in the virtual world. (As used herein, the terms buyerand seller respectively include buyer's bots and seller's bots.)

Near-perfect information requires, among other things, enoughinformation for the buyers and sellers to tailor their deal optimallyand for sellers to respond to their competitors' offers. Without thesetwo characteristics, either bargaining costs or competitive intelligencecosts would prevent efficient outcomes.

7. The Buyer can Withdraw.

The buyer's ability to withdraw its RFO allows the inexperienced buyeran opportunity to experiment, learn, and refine the description of herpreferences. This permits more effective search and bargaining andaffords the buyer an opportunity to discover whether there is anythingbeing offered that meets her needs. Without the right to withdraw, thebuyer might be too fearful of making mistakes in a multi-attributesearch to be willing to participate in the process.

8. The Auction can Operate in Real Time.

The process operates on-line and in real time. The time required tocreate and clear the market is a resource, and using it entails asignificant cost. Minimizing time and effort by both buyers and sellersreduces total cost for everyone.

Real-time operation can help to drive buyer search and bargaining costsclose to zero. If the auction is slow, the need to conduct repeatedauctions with different criteria could discourage the buyer, leading herto give up on searching for a better deal or trying to express herpreferences more precisely before gathering full information.

9. The System Provides a Recommendation to Buyers and Bid-RelevantInformation to Sellers.

Buyers receive recommendations about the desired product or service. Therecommendation is based on a complete evaluation of all dimensions ofall offers measured against the buyer's stated and/or previouslyarchived criteria. Sellers can receive information about the how winningbidders adjust their offers based on the buyer's stated preferences,utility function and profile.

This mechanism helps to create the conditions of near-perfectinformation. Without it, the buyer would be faced with an overwhelmingset of offers to assess and sellers would be left to guess about whatbuyers wanted.

B. A System to Minimize Total Transaction Costs

The features and attributes of the present invention—as described in thesection immediately above—will now be mapped onto the five types oftransaction costs that need to be minimized. A system is described whoseprocess directly attacks each of the goals identified previously.

In the preferred embodiment, the system is a service operated by anAuctioneer. Other embodiments of the system could include a privatelabel, embedded service provided on an outsourced basis to a thirdparty, e.g., Portal buyer's auction “powered by system name.”Alternatively, the system can be a non-labeled service operated by athird party. In such cases, the system is simply technology licensed tothe third party service provider.

Regardless of the specific business arrangement, the result is anAuctioneer service offering a buyer's auction with design goals that arespecifically targeted on minimizing a set of five interrelated costs, asfollows:

1. Minimize Market Intelligence Cost

a) Buyer Expresses Initial “I want to Buy” and Decision Criteria

The system preferably offers the buyer a natural language inquiry toexpress the initial “I want to buy . . . .”

The system preferably offers the buyer expert assistance on refining andstructuring the desired product/service in the form of an electronicdecision criteria template.

The system preferably supports the buyer's need to iterate on thetemplate, trying out various formulations, until the buyer is satisfiedthat she understands the relevant factors of a decision.

b) Buyer Sets Personal Priorities

The system preferably offers the buyer expert assistance in definingpersonal goals and tradeoffs in the form of a priorities template.

At the buyer's option, the system can provide a variety of automatedassistance to help the buyer set her priorities, e.g., archivalknowledge of what the buyer has opted for before, of the buyer's peergroup goals; and of third party experts' opinions on priorities.

c) Sellers can be Informed about Buyers.

Sellers can receive various types of information about a particularbuyer before, during, or after a buyer's auction with that buyer, if thebuyer permits. In addition, sellers can receive statistical informationfrom the buyers' “I wants” to help them assess what product offeringswould be most desired by their customers and what kinds of bids havebeen winning customers. This data mine is current and can take intoaccount all the proprietary information resulting from previous buyer'sauctions that occurred within the system.

d) Sellers can be Informed about Current Competitors.

Before bidding for a particular customer, sellers can receive the samesearch results as were available to the buyer, providing them withvaluable information about the current state of competition in themarket.

2. Minimize Search Cost

a) Buyer and Sellers can See all Competitive Posted Offers

The system applies the buyer's decision criteria and priorities templateagainst a database of instantaneous market choices (posted offers). Incontrast to systems that sell position in a list or that have a smalland exclusive set of sellers, this system can prioritize sellers usingthe buyer's own criteria.

The system provides a rich set of constraints that can be set by thebuyer (or set via defaults) to delimit searches, e.g., by geography, byvarious characteristics of the product; or by characteristics of theseller.

The system can select a subset of the market choices and presents themto both the buyer and sellers in a directly comparable format.

b) Buyer Receives an Evaluation and Recommendation

The system offers the buyer an evaluation and recommendation on the bestmatch between the buyer's decision criteria and priorities compared tothe posted market offers. This feature helps the buyer sift through themany offers to identify the ones that are most promising according toher specified criteria.

The system supports the buyer's possible need to iterate on her featurestemplate and priorities after she has seen the market choices. Thisiteration refines and solidifies the buyer's demand and moves her closerto a buying decision. Of course, a buyer can also choose to skip seeingthe posted offers and go directly to the buyer's auction.

c) Sellers have Relevant Customers Identified to them.

Sellers do not need to incur substantial advertising costs to findcustomers. Merely by providing a good deal to their target customers ontheir website, the relevant buyers will be identified to them, allowingthem to make an offer for free. The same information is scatteredthroughout the web (and is therefore non-proprietary), but the systemreduces the cost to the seller by providing it at the right time in theright form, i.e., precisely at the moment when a motivated buyer emergesand about the exact product or services that are relevant.

3. Minimize Bargaining Cost

a) Buyer requests an offer—“make me an offer”

The system creates a personalized auction for buyers. The systempresents bidding sellers (i.e., their automated proxies operating withinthe system's servers) with the buyer's statement of demand (i.e.,request for offer) and product feature decision criteria. In addition,if the buyer permits it, the system can provide sellers with the buyer'spersonal priorities template, archival data and profile.

b) Seller Sets Business Rules

The system presents bidding sellers with a business rule formatcontaining variables that map onto the buyer's priorities (e.g., totalcost and quality) with the goal of capturing the demand.

The system supports seller business rules that are either set within thesystem or are set on the sellers' servers (proprietary to the seller).

c) System Automatically Presents Adjusted Offers to Buyer And BiddingSellers

The system simultaneously presents to bidding sellers near-perfectinformation regarding the buyer's priorities, profiles, and all othercompetitors' posted offers compared to the sellers' posted offer.

The system adjusts bidding sellers' posted offers by applying sellers'business rules.

The system simultaneously presents the result of a request for adjustedoffers to buyers and to all other bidding sellers.

The system can present the buyer with an evaluation and recommendationon best matches between a buyer's decision criteria and priorities ascompared to sellers' offers. Evaluations and recommendations can begiven before, during, or after the auction process. Sellers can receiveanalogous information.

The system can present to bidding sellers an analysis of the transactionthat helps to explain why the winner captured the demand and/or why aseller lost.

d) Auctioneer can Automatically Attach Complementary Offers to the Buyer

The system (representing the Auctioneer) can present complementaryoffers that are related to and increase the value of the offer inresponse to the buyer's request for offer. The offers made by theAuctioneer (i.e., the service) can increase the value of one of thecriteria in the electronic template defining the characteristics of theproduct, e.g., product features or upgrades; or can increase the valueof one of the criteria concerning buyer preferences, e.g., betterfinancing, warranty or delivery terms. The auctioneer can attach suchcomplementary offers by purchasing these ancillary benefits from a thirdparty or by making an arrangement with a third party to attach theirservices to the adjusted offer. In this manner, the buyer is assuredthat using the service can always result in a benefit.

4. Minimize Seller Execution Cost

a) The System Archives Auction Data

The system captures all relevant data resulting from all phases of theauction, including data about why a purchase was not made. Thisminimizes the buyer's and sellers' need to exchange data, even when thebuyer is dealing with a new seller for the first time.

b) The System can Deliver a Complete Order to Seller and Confirmation toBuyer.

The system stores all relevant information necessary to create apurchase, transfer funds and effect delivery. This can reduce oreliminate the need for additional communication between the buyer andseller after the auction to consummate the transaction.

c) The System can Collect Funds from the Buyer and Transfer Them to theSeller.

The system provides a modern, secure and efficient transactionalenvironment. This provides assurances of delivery to the buyer andcollection to the seller, while also streamlining the process.

d) All Buyer's Data, Sellers' Data and Outcome of the Buyer's Auctioncan be Archived and Integrated as Inputs to the Next RFO Cycle.

The system captures all the data about an auction. A multi-round,iterative procedure can become burdensome if information is notintelligently archived and integrated. The system can perform thesefunctions.

5. Minimize Buyer Integration Costs

The entire experience—from first identification of a need, to search, tobargaining, to transaction, to post-sale service —can be considered asan integrated flow. This integration presents a major opportunity tominimize the total cost to both buyers and sellers. Even if each of theindividual stages in the pipeline described above were availableelsewhere, the total cost of doing them separately will always be higherthan the cost of doing them in a unified fashion.

a) Step-by-Step Content for Buyers

The system can provide the buyer with all the information and ancillarysupport services needed to complete successfully the entire process—fromfirst identification of a want through execution of a purchasetransaction.

b) Consistent User Interface for Buyers

The system maintains a consistent user interface for the buyerthroughout all stages of the process (market intelligence, search,bargaining and transaction execution).

c) Integration of Third Party Content

The system integrates within a common interface a rich variety ofdiverse, heterogeneous, third party content to provide marketintelligence for the buyer while maintaining a consistent environment.This extensible framework enables the system to add new templates asthey become available from third parties.

d) Templates for Communication Between Seller and Buyer

The system arranges for low cost communication between the buyers andsellers by providing common templates, e.g., for expressing featurepriorities and optimizing goals. This has two effects—it reduces theamount of time needed by buyers and sellers to enter data; and itprovides a protocol that makes the communication between buyers andsellers and the computation about the offer both relevant and efficient.

e) Automatic Archiving

The system leverages its institutional memory (archives) about allbuyers and all auctions—who won, who lost and why—to the benefit of bothbuyers and sellers. In most systems, the need to enter data is a majorinhibitor to use. This system captures data on the fly wherever possibleand can reuse it both for specific (individual buyer or seller) andaggregate (large-number auction analysis) purposes, provided that userprivacy concerns are addressed.

f) In-Situ Transaction

The system enables the buyer to accept an adjusted offer and consummatethe transaction within the Auctioneer's site. This removes from thebuyer the integration cost of interacting with a third party system(i.e., the successful seller), e.g., re-registering, learning tonavigate a new environment, and re-entering data that have already beenentered elsewhere before.

g) Post-Sale Service

The system supports near-automated post-sale service by maintaining acomplete archive of the transaction. The archives are maintained foreach buyer, each seller and each auction. In the event that post-saleservice is required, all the data about that transaction are availablein archived form. The archiving is fully automated. Hence, the creationof these archives requires no additional effort by the buyer or theseller. Moreover, the system ensures that the privacy concerns of theparties are met.

C. The System is Supported by a Robust Business Model

1. Goal—Maximize Participation and Information Revelation

The system business model is designed to encourage maximum participationby both buyers and sellers by keeping costs low for both sides of themarket. For buyers, the system is without charge and easy to use. Forsellers, no charges are levied that don't have offsetting benefits. Forsellers, the most basic level of participation (submitting postedoffers) costs nothing, thus removing all price barriers. There is noreason not to participate merely because many other sellers areparticipating.

At the same time, because low seller costs promote wide sellerparticipation in the auction, the buyer's incentive to conceal herpreferences is usually eliminated. Competition among the sellers usingthe buyer's stated preferences makes it difficult or impossible toexploit the buyer's preferences as could happen in a bargaining ornegotiation situation. Thus, the multidimensional auction itself solvesone of the key information problems.

2. Products and Pricing

The system creates value as an infomediary by delivering two kinds ofproducts to sellers: a market information product and an Auctioneersuccess fee product. The pricing structure should be set so that itremains robust as the business grows and the products should be reliableand difficult to redistribute in ways that preserve their value.

a) Information Products (for Sellers)

The system solves the problem of an apparent market failure forinformation by producing timely and relevant information for both buyersand sellers. Buyers receive all market and archival information at zerocost. Sellers can subscribe to a variety of information packages thatcan be bundled and price discriminated according to their perceivedvalue. A simple example is two bundles of information:

(1) Non-proprietary information bundle

The non-proprietary bundle theoretically should include all informationthat sellers could extract from the web with some effort, i.e., it iseither posted publicly or can be purchased by a subscription from thirdparties. For example, sellers can replicate the functionality ofextracting instantaneous comparisons of their products with all othercompetitive products then-posted on the web by using a sophisticatedsearch engine, e.g., the Inktomi Shopping Engine. Also, sellers canpurchase profile information from third party sources, e.g., Acxiom.However, a byproduct of the system is a clean, relevant and timelycompilation of those data. Those data are valuable and support therevenue model.

(2) Proprietary Information Bundle

The proprietary bundle theoretically should include all information thatsellers cannot extract from the web with effort, i.e., it is neitherposted publicly nor can it be purchased by a subscription from thirdparties. For example, sellers cannot replicate the functionality of theauction, hence sellers cannot see: (i) who bid, (ii) what elements ofthe offer they adjusted, (iii) who won the round, and (iv) why they wonthe round. However, a byproduct of the system is a clean, relevant andtimely compilation of these proprietary data.

Because the proprietary data are produced as a byproduct of the buyer'sauction, neither buyers nor sellers are required to expend additionaleffort (cost) to generate this information. Yet this information isextremely valuable to both buyers and sellers. The difference betweenits production cost (near zero) and its market value (high) representsthe potential profit margin that can be extracted by a service usingthis invention.

Importantly, the data generated within the system is most valuable whenused immediately. That is, the information is useful for making a saleto this customer who is requesting an offer right now. By running thesellers' bidding-offer bot internally on the system, no informationleaks from the system during the period when that information is mostuseful. Because the information does become available later, itsaccuracy is subject to verification, but not before its usefulness tothe seller has diminished.

Logically, the data that are exclusively available via the system as adirect result of the buyer's auction are valuable and provide supportfor the revenue model. The system reallocates the value gained by makinga transaction more efficient into a proprietary information product. Theprice of that product to sellers can always be set to be less than itsvalue. Thus, the reallocated benefits of a buyer's auction accrue inthree directions: to the buyer (by producing a zero-cost optimal resultin response to an “I want”); to the seller (by producing low costproprietary information that can be leveraged across all transactions);and to the system (by producing a robust and sustainable revenue streamfor the infomediary).

b) Auctioneer Success Fee Product

From the seller's point of view, the second product is the role of thesystem as Auctioneer and the opportunity to participate in the buyer'sauction. The business model for traditional (Yankee) auctions iswell-established on the web. Companies such as eBay extract between 2.5%and 7% of the value of a completed transaction. Because these are“success fees”, they logically do not inhibit sellers from becomingactive bidders. And because the auction is fully automated, other costsof participating are also low enough not to discourage participation. Aswith all success fees, only the winner pays. Everyone else has a freeride. The purpose of the information products (above) is to more fairlyallocate the cost of running the system between all sellers.

When compared to other auctions that charge an identical transactionsuccess fee, this system produces greater value for sellers. The reasonis that most auctions (or posted price sales) randomly perform a bestmatch between buyer's demand (utility function) and sellers' offers(production function). This system is optimized to perform the bestmatch. The difference between the optimized approach must produce equalor greater value for sellers versus the random approach. Hence, evenwhen comparing two systems with identical success fees, this system willbe more beneficial to sellers.

3. Revenue Sources Eliminated from the Business Model

An important goal of a buyer's auction is to encourage as many buyersand sellers as possible to participate. Markets work best when the mostpeople play in a competitive environment with near-perfect information.Anything that unfairly discourages buyers and/or sellers fromparticipating should be eliminated.

Note that the core revenue model specifically excludes several types ofrevenue so as not to create disincentives and resistance for eitherbuyers or sellers. The presence of these revenue sources coulddiscourage buyers and/or limit the number of sellers.

“Pay to play” fees are a small entry fee, as in the “ante up” in a cardgame. These fees are eliminated because they discourage the bidders wholose repeatedly (small market share) at the expense of larger sellers.Discouraging such bidders would prevent the number of sellers fromgrowing as the business grows.

Two other sources of revenue are eliminated because they would likelyundermine buyer confidence in the objectivity of the system'srecommendations. One such source is banner advertising fees, which mayboth raise buyer suspicions and skew the auction in favor of largersellers who are able to advertise heavily. This is the same principle asbarring campaign advertising next to a polling booth during an election.

Position placement fees are eliminated because they unfairly skew theresults of the auction in favor of larger sellers who can purchase aposition even though their total score was not the highest. Also, theythrow the credibility of the system into doubt in the buyer's mind, thusinhibiting buying.

V. Application Scenarios—Commerce Themes

The mechanism of a buyer's auction has wide applicability. The presentinvention can be applied to many situations including goods andservices, from low end to high end. The particular framing of the “Iwant to . . . ” illustrates the range of applications. In some cases,goods and services are bundled together, e.g., a contractor building ahouse; an auto mechanic repairing a transmission.

“I want to . . . ” examples include, without limitation, the followinggoods and services:

A. Health

goods services high end intensive home care non-elective surgicalprocedure equipment e.g., that needs to be reserved quickly, monitoringinstruments e.g., heart bypass, ovarian cancer uninsured prescriptionhealth insurance drugs low end insured prescriptions minor electiveclinical procedure, drugs e.g., dental hygiene drug store SKUs couponsfor personal hygiene services

Goods:

“I want to rent a convalescing bed for three months . . . ”

“I have five prescriptions I need to fill every month. I want to get aprice for the whole bundle from one company . . . .”

“I want to do my drug store shopping list . . . ”

Services:

“I want to find a surgical center that can schedule a ______ procedureimmediately . . . .”

“I want to find a better group health plan for our small business group. . . ”

“I want to receive offers from physical therapy centers near myhome—have a sports injury to my knee. Doctor prescription . . . .”

B. House & garden

goods services high end new house install new roof living room furniturehome insurance low end bed and kitchen goods garden maintenance grocery,household supplies ISP newspaper delivery

Goods

“I want to buy living room furniture—must have great financing . . . ”

“I want to get a mid-range refrigerator. Include installation anddelivery . . . .”

“I want to subscribe to five magazines, a book club and a DVD club . . .”

“I want to get coupons to the following list of items . . . ”

Services:

“I want to buy a house for lower commission than 6% . . . ”

“I want to refinance my home . . . ”

“I want to get an ISP that can host my family web page . . . ”

“I want to get the best telephone rates . . . ”

“I want to be find all department store sales the minute they start . .. ”

“I want to join . . . ”

C. Personal Finance

goods services high end computer and SW mortgage, insurance brokerageaccount credit & debit cards low end tax return, CPA commercial bankservices traveler's check, foreign currency

Goods

“I want to get a PC and printer and tax preparation software . . . ”

Services:

“I want to refinance my home . . . ”

“I want to I want to do online trading and get low commissions . . . ”

“I want to get new home insurance including earthquake . . . ”

“I want to get better rates and higher limits on my credit card . . . ”

D. Automobile

goods services high end car major collision repair major car parts,audio system auto insurance major mechanical repair low end replacementcar parts, tires oil change gasoline

Goods

“I want to buy a car . . . ”

“I want to get an audio system for my truck . . . ”

“I want to find a great deal on tires . . . ”

“I want to get coupons for gasoline . . . ”

Services:

“I want to find a body shop to fix my car—insurance claim . . . ”

“I want to find auto insurance for my 16 year old boy . . . ”

“I want to get a complete transmission overhaul . . . ”

“I want to get 60K maintenance done on my 1993 Ford Taurus . . . ”

E. Leisure, travel, entertainment & sports

goods services high end pleasure boat luxury cruise, all-inclusive newgolf clubs resort weekend getaway theater, pro sports low end sports andcamping equipment dance clubs luggage restaurants sports clothing movieCDs, books, videos, DDS

Goods

“I want to buy a power boat . . . ”

“I want to find a set of golf clubs . . . ”

“I want a complete set of camping equipment for a family of four—tent,sleeping bags, cooking equipment . . . ”

Services:

“I want to book a cruise to Mexico including airfare and tripcancellation insurance . . . ”

“I want to rent a car for a week in Florida . . . ”

“I want to find a weekend getaway for two in Napa . . . ”

“I want to go swing dancing tonight . . . ”

“I want to find a French restaurant, reservation at 8:00 p.m. for four .. . ”

F. Small Office, Home Office

goods services high end business machines office rent office furnituregroup health insurance plan legal and accounting services low end officesupplies telecommunication, ISP janitorial

Goods

“I want to buy five PCs for my company . . . ”

“I want to get a [particular brand] office setup . . . ”

“I want to find a supplier of [part brand] ink cartridges . . . ”

Services:

“I want to rent an office, 2,000 sq ft, Palo Alto area . . . ”

“I want to get a group health plan for five people . . . ”

“I want to get bids for our telephone service . . . ”

“I want to find a janitorial service, daily . . . ”

G. Brokers and agents

goods services high end — agent to sell my house art auction agent tosell my car low end — stock broker insurance broker consignment agentfor furniture travel agent

Services:

“I want to sell my house and want bids on commission rates . . . ”

“I want to sell my antique bed and want bids from antique dealers onconsignment rates . . . ”

“I want to trade on-line and want bids on commissions . . . ”

“I want to buy air travel and want bids on commissions . . . ”

H. Education

goods services high end PC, printer private college technical andvocational training low end books college prep course school suppliesself-help course

Goods

“I want to get all the school supplies for my five kids, one time . . .”

Services:

“I want to find a college that can give me a scholarship . . . ”

“I want to learn Spanish . . . ”

“I want to receive technical training on ______ (subject) . . . ”

“I want an SAT prep course . . . ”

“I want to get re-certified as a real estate agent in California . . . ”

I. Personal Matters

goods services high end — lawyer accountant private investigator low end— employment counselor tutor

Services:

“I want to change jobs . . . ”

“I want to track down my long-lost aunt . . . ”

“I want to find a divorce lawyer that charges less than . . . ”

“I want to find someone to do my taxes right away . . . ”

“I want to find a masseur who knows how to . . . ”

“I want to find someone to teach me how to . . . ”

“I want to find an organization that . . . ”

“I want to join a club that does . . . ”

“I want to find a great yoga center . . . ”

“I want to find a (man, woman) for a casual relationship . . . ”

J. Electronics & Computers

goods services high end home theater technical consultant PC, printer,etc. installation & repair low end portable tape player ISP batteries,cables, supplies

Goods

“I want to get the following supplies for my office, on a regular basis. . . ”

Services:

“I want to get an expert to set me up with PCs, software and teach mehow to use it . . . ”

“I want to find someone to design, install and maintain a home theater .. . ”

The various embodiments described above should be considered as merelyillustrative of the present invention and not in limitation thereof.They are not intended to be exhaustive or to limit the invention to theforms disclosed. Those skilled in the art will readily appreciate thatstill other variations and modifications may be practiced withoutdeparting from the general spirit of the invention set forth herein.

VI. Mitigating Potential Negative Effects: Auction Design Rules

Every market has the potential to be distorted or corrupted. The presentinvention contemplates such negative influences. The auction rules aredesigned to minimize or completely eliminate undesirable side effects.In that sense, the invention encompasses methods and constraints on itsown mechanism, much as the preferred embodiment of a jet engine mightcontain sensors, governors and fire extinguishing systems to keep itfrom causing damage.

A primary method of mitigating the potential negative effects of thispowerful market mechanism is embodied in the auction design rules. Thoserules are an extensible framework that can be changed as potentiallypernicious symptoms become apparent. For example:

A. Collusion and price fixing

When sellers behave competitively, perfect information helps to supportperfect market outcomes. However, when sellers are seeking to collude,excessively good market intelligence can make collusion easier. Therules of the auction and the information products can be adjusted tomake collusion and price-fixing more difficult. For example, in highlyconcentrated markets, it may be desirable to hide the identity of thebidders to limit the use of that information to enforce a collusiveagreement. Adding such a feature has been contemplated and can be builtinto the auction design rule.

B. Predatory Pricing

Similarly, bidders in an open auction could, in principle, use theinformation to prevent a particular competitor from obtaining anybusiness, or any business that meets certain profitability criteria.Again, the auction rules can be designed to hide enough information tomake such strategies difficult or impossible. Adding such a feature hasbeen contemplated and can be built into the auction design rule.

C. Price Discrimination

A potential concern about the distribution of so much information tobidders is that they will use it to engage in price discrimination.Indeed, we would expect that bidders might well bid aggressively for theconsumers who are most likely to bring repeat business or to purchaseprofitable products. The result may well be lower prices for richercustomers.

One solution to this problem is group-buying. The present invention caninclude group-buying features. Adding such a feature has beencontemplated and can be built into the auction design rule.

In addition, the present invention gives buyers control over whatinformation about themselves can be given to prospective sellers.

SUMMARY OF THE INVENTION

The present invention is a methodology, system and business model forfacilitating an online buyer's auction in which the major categories oftransaction costs are significantly reduced by providing the buyer andthe sellers with near-perfect information about one another.

The process is initiated by the buyer, who is assisted in creating atemplate that specifies her preferences to potential sellers. Thebuyer's request for an offer is based on a flexible “I want” thatenables a fast, comprehensive search to be conducted to provide relevantfeedback based on the buyer's preferences.

During the auction period, seller bots adjust the sellers' offers basedon near perfect information about the buyer and the competition. Thisnear perfect information can include detailed information about thebuyer's current preferences, demographics, and previous buying history.In addition, it includes detailed information about competing sellers'offers. The offers are multidimensional, i.e. based on more than justprice, which allows sellers to tailor their offers to the buyer. The useof seller bots enables fully automated submission of sellers' offers tothe buyer in real time.

The system also provides an automated recommender to help the buyeridentify the best offers according to the buyer's preferences.

The system provides the basis for an integrated solution to marketintelligence, search, bargaining and transaction execution costs,thereby eliminating the integration costs of a piecemeal approach. Inaddition, the system provides the support for a revenue model for thesale of proprietary and non-proprietary marketing information derivedfrom the auction.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a block diagram illustrating the overall structure of thepresent invention.

FIG. 2 is a block diagram illustrating one embodiment of the corenetwork.

FIGS. 3-22 are block diagrams illustrating exemplary embodiments of theindividual components of the present invention.

FIG. 23 is a block diagram illustrating the communication of informationbetween buyer interface and web server, and between seller interface andweb server.

FIGS. 24-27 are flow charts illustrating an exemplary embodiment whereinthe buyer creates a request for offer (RFO), specifies priorities, andreceives adjusted offers.

FIG. 28 is a flow chart illustrating an exemplary embodiment of abuyer's auction.

FIGS. 29-30 are flow charts illustrating a process in which the sellerspecifies business rules, runs a simulation, and requests and obtainsinformation generated by the present invention.

FIGS. 31-60 are diagrams illustrating exemplary computer displays seenby users of the present invention.

FIG. 31 is an exemplary user interface U100 in which a buyer enters adescription of the product or service she wants to purchase.

FIG. 32 is an exemplary user interface U200 that displays research oradvice requested by a buyer.

FIG. 33 is an exemplary user interface U300 that displays a buyer'spriorities for product or service features.

FIG. 34 is an exemplary user interface U310 that lets a buyer choose thelevel of expert assistance provided to the buyer.

FIG. 35 is an exemplary user interface U400 that lets a buyer constrainher search.

FIG. 36 is another exemplary user interface U410 that lets a buyerconstrain her search.

FIG. 37 is an exemplary user interface U500 that lets a buyer create anautomated bot.

FIG. 38 is an exemplary user interface U600 that displays initial selleroffers to a buyer.

FIG. 39 is an exemplary user interface U700 that displays value scoresfor seller offers.

FIG. 40 is an exemplary user interface U800 with a buyer registrationform.

FIG. 41 is an exemplary user interface U810 that lets a buyer limit thenumber of seller offers displayed to the buyer.

FIG. 42 is an exemplary user interface U900 that displays a list offinal adjusted offers along with a score for each offer.

FIG. 43 is an exemplary user interface U910 that includes value addedproducts or services or other offers to enhance the overall offering tothe buyer.

FIG. 44 is an exemplary user interface U1000 that lets a buyer execute atransaction.

FIG. 45 is an exemplary user interface U1100 that shows an adjustedoffer evaluated with respect to a buyer's priorities.

FIG. 46 is an exemplary user interface U1200 that displays the resultsof a suggestion search.

FIG. 47 is an exemplary user interface U1300 that lets a buyer accessinformation related to the buyer that is stored in a database.

FIG. 48 is an exemplary user interface U1310 that displays an archivedrecord of a buyer's transactions.

FIG. 49 is an exemplary user interface U1320 that shows a report of arewards program for a buyer.

FIG. 50 is an exemplary user interface U2000 that provides an overviewto a seller, with links to sections discussing the rights andresponsibilities accepted by the seller.

FIG. 51 is an exemplary user interface U2100 that illustrates possibletypes of affiliation.

FIG. 52 is an exemplary user interface U2200 that summarizes exemplarytypes of information available under each type of affiliation.

FIG. 53 is an exemplary user interface U2300 for specifying a seller'sbusiness rules.

FIG. 54 is an exemplary user interface U2400 for specifying a seller'sloyalty program.

FIG. 55 is an exemplary user interface U3000 that shows informationabout an anonymous buyer that may be seen by a seller.

FIG. 56 is another exemplary user interface U3100 that shows informationabout an anonymous buyer that may be seen by a seller.

FIG. 57 is an exemplary user interface U3200 that shows records ofposted offers that may be seen by a seller.

FIG. 58 is an exemplary user interface U3400 that shows records ofadjusted offers that may be seen by a seller.

FIG. 59 is an exemplary user interface U3500 that displays the terms ofan offer eventually accepted by a buyer.

FIG. 60 is an exemplary user interface U3600 that displays aggregateinformation about and analysis of auctions occurring during a certaintime interval.

DETAILED DESCRIPTION

I. System Architecture

The architecture of a preferred embodiment of the invention is shown inblock diagram form in FIGS. 1-22. The implementation of the architecturewill be readily apparent to those skilled in the art because of the useof standard components and technologies, and need not be described indetail here because their use, functionality and interrelation will bereadily apparent in the next section (System Operation).

Various aspects and features of the invention will now be described withrespect to an exemplary embodiment of the invention as shown in FIGS.1-22. However, this embodiment is just one of many possiblealternatives. There are a variety of ways in which one of ordinary skillin the art might architect the invention, including combining manyservers and databases into a more condensed architecture to utilize lessphysical hardware. Tradeoffs of serviceability, service availability,scalability and other reasons could lead a designer of ordinary skill inthe art to architect the service in a different configuration than thatdescribed herein, without departing from the general spirit of theinvention.

In addition, this technology could be implemented in whole or in part bythird parties as a service, as an outsource service, as a desktop orserver application or suite thereof, in a variety of manners that arewell known to those of ordinary skill in the art and which need not bedescribed further here.

Referring now to FIG. 1, TELEPHONE A100 is a standard telephone attachedto a telephone service (but could also be implemented as an IP basedtelephone, wireless telephone, satellite telephone, or other similardevices which generally include a speaker and a microphone).

In this embodiment TELEPHONY INTERFACE SERVER A200 includes a telephoneinterface card (such as those from Dialogic or NMS) as well as centralprocessing unit (CPU), memory, disk drive, operating system, networkadapter (such as Ethernet) and other components typical of networkservers. This server provides access to the telephone system using T1 oranalog lines, but could also provide access through voice over IP. Theserver typically has the capability to ring (place calls) as well asanswer telephone lines. It can receive and capture electronically,typically with .wav or similar file types, what the buyer is saying andeither store this information, or send it to the SPEECH RECOGNITIONSERVER A300 which can interpret the spoken material and compare itagainst a known grammar. Of course, A200 and A300 could easily becombined into a single physical server.

BUYER INTERFACE A400 is typically a standard computer, such as thosewhich run the Microsoft Windows or Apple Macintosh operating systems,but could also be a network computer (a simple terminal with a smalloperating system), a screen based telephone, a WebTV box (or similar), ahand-held computer with remote communication capability, or otherdevices which could be used to interface with information on the WorldWide Web of the Internet. Such devices may use a modem (line, cable,satellite, ADSL, wireless etc.) or network adapter to a Local AreaNetwork connected to the Internet, to interface with information on theInternet. The BUYER INTERFACE A400 may use an Internet Service Providerto connect to the Internet, but may utilize other means as well. A buyerwould typically utilize such a device in order to view and interact withweb pages.

INTERNET as referred to in the drawings includes a worldwide network ofinterconnections and routers connecting computers and databasesworldwide, which is also typically accessible locally by InternetService Providers and other means.

BUYER WEB SERVER A500 is a standard server that serves web pages (whichmay contain HTML, Java, ActiveX and other constructs). Such servers mayrun applications such as Apache, Microsoft IIS, Netscape SuiteSpot, orother web server software. It is used to provide access to web pages tobuyers and others using BUYER INTERFACE A400 or the like.

HTML DATA INTERFACE METHOD SERVER A600 is used to interface with theSELLER WEBSITE A700 by reading HTML and other web pages utilizing theInternet or by other well known methods to send and receive such data.It has a CPU, memory, disk drive, operating system, network adapter(such as Ethernet) and other components typical of network servers.

SELLER WEBSITE A700 is a standard server that serves web pages (whichmay contain HTML, Java, ActiveX and other constructs). Such servers mayrun applications such as Apache, Microsoft IIS, Netscape SuiteSpot, orother web server software. It allows users of the INTERNET to accesssellers' websites.

DIRECT DATABASE ACCESS METHOD SERVER A800 is used to interface directlywith the seller database A900 using the Internet or other methods tosend and receive such data. It has a CPU, memory, disk drive, operatingsystem, network adapter (such as Ethernet) and other components typicalof network servers.

SELLER WEB SERVER A1000 is a standard server which serves web pages(which may contain HTML, Java, ActiveX and other constructs). Suchservers may run applications such as Apache, Microsoft IIS, NetscapeSuiteSpot, or other web server software. It is used to provide access toweb pages to sellers using SELLER INTERFACE A1100 or the like.

SELLER INTERFACE A1100 is typically a standard computer, such as thosewhich run the Microsoft Windows or Apple Macintosh operating systems,but could also be a network computer (a simple terminal with a smalloperating system), a screen based telephone, a WebTV box (or similar) orother devices which could be used to interface with information on theWorld Wide Web of the Internet. Such device may use a modem (line,cable, satellite, ADSL, wireless etc.) or network adapter to a LocalArea Network connected to the Internet, to interface with information onthe Internet. The SELLER INTERFACE A1100 may use an Internet ServiceProvider to connect to the Internet, but may utilize other means aswell. A seller would typically utilize such a device in order to viewand interact with web pages.

CORE NETWORK A1200 contains most of the database and other serversdirectly involved in running the auction and other parts of the service.

NATURAL LANGUAGE INTERPRETER SERVER A1210 contains a CPU, memory, diskdrive, operating system, network adapter (such as Ethernet) and othercomponents typical of network servers, as well as a natural languageinterpreter application and several databases required for understandingnatural language.

BUYER DATABASE SERVER A1220 contains a CPU, memory, disk drive,operating system, network adapter (such as Ethernet) and othercomponents typical of network servers, as well as a database application(such as those from Oracle). Buyer information, such as buyerdemographics, buyer behavior, open profile standard data etc., is storedand retrieved here.

PRODUCT QUALIFIER DATABASE SERVER A1270 contains a CPU, memory, diskdrive, operating system, network adapter (such as Ethernet) and othercomponents typical of network servers, as well as a database application(such as those from Oracle). It contains information on categories usedto qualify products (such as those from Consumer Reports or othersources of product and service reviews).

SHOPPING ENGINE SERVER A1230 contains a CPU, memory, disk drive,operating system, network adapter (such as Ethernet) and othercomponents typical of network servers, as well as a web-based shoppingengine application, and also may contain a database application (such asthose from Oracle), and product and seller databases, including bothaffiliated and unaffiliated sellers. It retrieves and may organizeinformation on products from various sellers, including, withoutlimitation, the price, product features, brand, delivery time,warranties, shipping costs, financing, and seller reputation.

SELLER RULES DATABASE A1240 contains a CPU, memory, disk drive,operating system, network adapter (such as Ethernet) and othercomponents typical of network servers, as well as a database application(such as those from Oracle). It contains rules which affiliated sellershave set in relation to limits and other constraints on how selleroffers on products or services will be made to buyers through theauction process. This database need not reside within the core networkA1200. It could, for example, also reside at a secure seller site.

VALUE-ADD DATABASE SERVER A1290 contains a CPU, memory, disk drive,operating system, network adapter (such as Ethernet) and othercomponents typical of network servers, as well as a database application(such as those from Oracle). It contains information on value addedproducts or services or other offers which may be combined with selleroffers in order to enhance the overall offering to the buyer.

AUCTION ENGINE SERVER A1250 contains a CPU, memory, disk drive,operating system, network adapter (such as Ethernet) and othercomponents typical of network servers, as well as an auction engineapplication, and also may contain a database application (such as thosefrom Oracle), and a database of algorithms used for various productcategories as required. The server conducts the auction and generatesand stores results and reports for sellers.

THIRD PARTY DATABASE SERVER A1280 contains a CPU, memory, disk drive,operating system, network adapter (such as Ethernet) and othercomponents typical of network servers, as well as a database application(such as those from Oracle). It contains information on third partyratings of products and/or services which can be utilized in the auctionprocess depending upon buyer-selected priorities.

BILLING SERVER A1260 contains a CPU, memory, disk drive, operatingsystem, network adapter (such as Ethernet) and other components typicalof network servers, and may also contain a database application (such asthose from Oracle). It collects and maintains billing records. It mayprocess bills to invoice affiliated sellers based upon a variety ofcriteria, including, without limitation, completed sales, charges forparticipating in auctions, or charges for proprietary and/ornon-proprietary information.

II. System Operation

The preferred embodiment includes several methods for buyers to navigatethrough the process, which depend on the buyer's level of expertise.Buttons can be selected which give more information or which skipseveral steps altogether to take the buyer directly to the auctionitself, depending upon buyer preference.

FIG. 23 illustrates an exemplary embodiment of the present invention inwhich communications between buyers and sellers take place over theInternet, with buyer web server A500 and seller servers A600, A800 andA1000 acting as intermediaries. The buyer logs on to buyer web serverA500 and creates a request for offer (RFO) 10. RFO 10 is made availableto seller rules 60 that had been previously defined by sellers,transmitted to seller web server A1000, and stored within core networkA1200. An auction is run within the core network, taking into accountthe RFO 10, seller rules 60, and, potentially, some third partyinformation. Buyer web server A500 sends sellers' initial offers 40and/or adjusted offers 50 to the buyer, who decides whether or not toproceed with the transaction. If a transaction is concluded, purchaseannouncement 30 is communicated to the seller.

FIGS. 24 through 27 are flow charts illustrating the process by whichthe buyer formulates RFO 10, requests and receives initial offers 40,specifies her preferences 20, requests and receives adjusted offers 50,modifies her RFO 10 or preferences 20 based on adjusted offers generatedby the system, and carries on the transaction. It is assumed that buyerhad already established a connection with buyer web server A500, throughbuyer interface A400. Any computer capable of running web browsersoftware, such as Netscape Navigator or Microsoft Internet Explorer, canserve as buyer interface A400. The actual process of establishing suchan Internet connection to server A500 is well-known, and need not bedescribed further here.

At step 100 in FIG. 24, the buyer creates an RFO 10. In a preferredembodiment, video monitor A405 of buyer interface A400 displays a formsimilar to U100 (FIG. 31). In the form U100 (FIG. 31), a buyer enters adescription of the product or service she wants to purchase, thedescription preferably being made in natural language. The descriptionmay include the type of product, requested features, warranty period,financing needs, delivery preference, and any other attribute the buyerwishes to include. The description, however, can be also very general.For example, the buyer may specify that she is looking for productsenabling her to watch movies or for products enabling her to store food,rather than specifying particular items like VCRs and DVD players orrefrigerators and kitchen cabinets, respectively. The description isreceived by buyer web server A500, which passes it to natural languageinterpreter A1210, embedded within core network A1200, to convert itinto a format that shopping engine A1230 can later process. In anotherembodiment, the buyer selects the product category and features from apre-defined on-screen or pull down menu, which may be hierarchicallystructured.

At step 150, the buyer decides whether or not she wants to requestinformation or advice on a product or category of products. This may bedone, for example, by clicking on the “learn” button in form U100 (FIG.31). In another implementation, information is displayed automatically,depending on the vagueness of the buyer's description. Descriptions thatdo not include a precise specification of a product or service, but onlyan area of interest, are treated to suggest the buyer needs to beinformed about products or services in that area. In yet anotherimplementation, the buyer may actually begin with step 150, and proceedto step 100 only after having been educated about products fitting herneeds.

At step 170, video monitor A405 displays requested research or advice,through a form similar to U200 (FIG. 32). The research or advice issupplied to buyer interface A400 by third party data server A1280,through buyer web server A500. The information supplied based on theresearch request can vary in its complexity. For example, withoutlimitation, the information can be as simple as an article explainingthe available features of new products and the differences among them oras detailed as a table summary with feature-by-feature productcomparisons like those often shown in consumer magazines (e.g., ConsumerReports). Advice can range from a mere recommendation of a brand name,to a full stipulation of product's essential features, or to summarystatistics showing the popularity of various products among users of thepresent invention.

At step 200, the buyer can optionally delimit the scope of sellersearch, through a form such as U400 (FIG. 35) or U410 (FIG. 36), whichmay be accessed by selecting the “look only” button on form U100 (FIG.31). A wide variety of constraints can be placed on the search. Forexample, the buyer can limit eligible retailers to only those within alocal geographical area, state, or country. She can also excluderetailers from a particular geographical area, e.g. “everything butCalifornia”. Another limit may be imposed by specifying the highestprice the buyer is willing to pay, or the shortest period of warrantyservice. The buyer can also insist on including in the search only thoseretailers that were ever rated by reputable agencies, or reviewed bymajor magazines, or earned a high reputation from other buyers, possiblywith similar demographic characteristics. The buyer's constraints arestored in the Buyer Database Server A1220. In an alternative embodiment,step 200 may be omitted. In yet another alternative embodiment, step 200can be embedded after step 300.

At step 250 the buyer may choose to proceed directly to thespecification of her preferences and the actual auction, both of whichare described later in this section. This may be done by clicking on the“go!” button in form U100 (FIG. 31), form U400 (FIG. 35), or form U410(FIG. 36). The choice is for convenience to repeat buyers, who arefamiliar with the interface and aware of the time saved by using thisshortcut. In another embodiment of the system, it need not beimplemented. By clicking “my choices” in form U410 (FIG. 36) in buyerinterface A400, the buyer does not proceed directly to the auction,which makes the present invention comparable in “look and feel” tocurrent Internet shopping engines, thereby lowering the switching coststo users.

At step 300, shopping engine server A1230 queries product qualifierdatabase server A1270, and retrieves offers that satisfy most or all ofthe criteria specified in RFO 10. The results of the search, initialoffers 40, are passed to buyer interface A400, where they are displayedin form U600 (FIG. 38). Sellers offers may either be precompiled andstored on product qualifier database server A1270, or server A1230 mayrequest them and compile them on the fly from seller web server A1000,direct database access method server A800, or HTML data interface methodserver A600.

The buyer may sort returned initial posted offers 40 in U600 (FIG. 38)by price, delivery time, store distance, seller name, manufacturer name,model number, etc., by clicking on the appropriate buttons. In anotherembodiment, the posted offers could be sorted by a score that isautomatically imputed to each offer, as described in greater detail instep 380.

Optionally, the system could, at this stage, enrich the list of initialoffers by a list or browser window displaying complementary goods orservices. Complementary or substitution products may, withoutlimitation, be identified by analysis of buying habits of consumers orby the application of a collaborative filter to the buyer's request. Inother embodiments, similar suggestions could be made, withoutlimitation, at steps 380, 1300, 1620, or 1900.

At step 350 the buyer can revise her RFO 10, by displaying the form U100(FIG. 31) (or a similar form) again. This helps in situations in whichRFO 10 was stipulated too narrowly, with shopping engine A1230 returningonly a few or no initial offers 40, or too broadly, when hundreds ofoffers 40 were returned U600 (FIG. 38). Alternatively, this step can beomitted, leaving buyers to use other methods to return to step 100, suchas pressing the web browser's “back” button.

At step 370, the buyer asks for a recommendation from among the initialoffers 40, for instance, by clicking on the “make a recommendation”button in form U600 (FIG. 38). Alternatively, the recommendation may begenerated automatically, without the buyer's prompt, when the postedoffers are initially displayed.

At step 380, the recommendation is displayed by buyer interface A400 ina suitable form. A possible form is shown in U700 (FIG. 39), wherein anumerical score is calculated for each initial offer 40 and offers aresorted in descending order. Such a score could, for example, be based inpart on the ranking of the product by Consumer Report and/or othermagazines, or it could be based in part on its popularity among otherbuyers, as determined from records of purchases.

At step 400 buyer chooses to proceed with an auction or to make animmediate transaction. In one embodiment, buyers conducting immediatetransactions (i.e., not using the auction component of the presentinvention) do not need to identify themselves because they transfer tothe seller's web site to conclude the transaction, while buyersrequesting adjusted offers 40 must be registered. In alternativeembodiments, all buyers may be required to conclude every transactionin-situ, thus requiring identification from all of them. In yet anotherembodiment, all transactions may be concluded directly with the seller,for example at his website, thus requiring no registration from anybuyer at the Auctioneer site.

At step 500, the System checks whether the buyer has registered withbuyer web server A500 before. If not, a standard registration form U800(FIG. 40) is displayed on buyer interface video monitor A405, in whichthe buyer identifies herself. This step can also be automated, forexample by using browser cookies, thus demanding no action on thebuyer's part.

In the present embodiment, registration and identification are used tocreate and invoke buyer's profile, stored within buyer database serverA1220. A simplified version of the system may not require step 500.Instead, buyers could re-enter information concerning their prioritiesevery time they use the simplified system.

At step 600, the buyer completes a registration process. Buyer webserver A500 instructs buyer database server A1220 to open a new“account”, and the buyer sees, for example, a form such as U1300 (FIG.47) on her monitor A405. The buyer or her proxy enters information aboutthe buyer which can include, without limitation, basic personaldemographic information, billing and shipping addresses, and credit cardinformation, which are stored in buyer database A1220. The buyer'saccount information is preferably accessible to the buyer from any userinterface so that it can be updated or modified by the buyer at anytime.

Form U1300 (FIG. 47) makes accessible other forms, like U1310 (FIG. 48),U1320 (FIG. 49), U300 (FIG. 33), or U310 (FIG. 34). Form U1310 (FIG. 48)displays an example of buyer's archive record, showing all transactionsthat the buyer made within the system. Form U1320 (FIG. 49) shows areport of a rewards program. Sellers may offer benefits in terms of areward program to the buyer, as part of their bidding strategy and/or inexchange for information about the buyer. Forms U300 (FIGS. 33) and U310(FIG. 34) deal with the buyer's priorities and are discussed later inthis section.

At step 700, the buyer chooses whether to create a new set of priorities20 or to use her priorities 20 stored in her account on buyer databaseserver A1220. For example, buyers who frequently purchase the same orsimilar goods may benefit from using their stored priorities 20, whichhad already been optimized. At step 800, buyer web server A500 contactsbuyer database A1220 to recover stored priorities 20. They are, in turn,passed to buyer interface A400, and displayed in a form such as U300(FIG. 33). The sliders in form U300 (FIG. 33), which correspond to thebuyer's priorities for product or service features, can assume theirpositions from the last transaction, or their positions when last storedin the buyer's account.

At step 1000, buyer's approval of the recovered priorities 20 is sought.In form U300 (FIG. 33), the priorities 20 may be approved by clicking onthe “go!” button. At step 1100, the buyer modifies recovered priorities20. This modification can be done in a wide variety of ways. Forexample, the modification can be made by adjusting the sliders in anexemplary form U300 (FIG. 33). It can also be made with the aid of anexpert system, as illustrated by the “decide for me” button on form U310(FIG. 34). The expert system may run on buyer database server A1220, orany other server within core network A1200, or be dedicated to its ownserver. The expert system may, for instance, analyze the buyer'stransaction record and infer the most likely priorities 20 that wouldhave generated such a record. It may also base its suggestion on theaverage or median priorities 20 of a group of buyers with similardemographic characteristics.

At step 900, the buyer creates a new set of priorities 20 by movingsliders within form U300 (FIG. 33). Sliders are just one example of themany ways that could be used to enable a buyer to set her priorities.Other methods of setting preferences are well known to those of ordinaryskill in the art and need not be described in detail here. Optionally,expert system aid may be available at step 900.

At step 1150, buyer instructs buyer web server A500 to store the new ormodified priorities 20 in her account within buyer database A1220. Theactual storing of priorities 20 is done in step 1175.

At step 1180, the buyer can optionally put restrictions on displayedauction results. For instance, as shown in an exemplary form U810 (FIG.41), the buyer can limit the number of adjusted offers 50 to bedisplayed, or provide a cut-off point for adjusted offers 50. Buyer mayalso be reminded at this step of the restrictions created in step 200,in forms U400 (FIG. 35) and U410 (FIG. 36). In another embodiment, step1180 may be omitted.

At step 1200, auction engine server A1250 runs a buyer's auction. Thedetailed description of the auction process is provided later below,using FIG. 28 with steps 1210 through 1280.

In certain cases, as in U910 (FIG. 43), utilizing A1290, it may bebeneficial for the Auctioneer (the buyer's auction service provider) toattach value added products or services or other offers which may becombined with seller offers to enhance the overall offering to thebuyer. This may also give the perception to the buyer that all offersare adjusted whether or not they are from affiliated sellers.

At step 1300, a list of final adjusted offers 50, with their scores, isreturned to the buyer web server A500 by auction engine server A1250. Itis passed to buyer interface A400, through an exemplary form U900 (FIG.42). The results may be sorted in a wide variety of ways, includingwithout limitation, by the score each adjusted offer 50 earned, byprice, or by model number.

At step 1400 buyer determines whether to proceed or to modify herpriorities 20. For instance, by clicking on the “adjust my priorities”button in form U900 (FIG. 42), the buyer returns to step 700. The loopgives the buyer a quick way to learn how different sets of priorities 20affect the resulting adjusted offers 50. Step 1400 is not essential,other embodiments need not contain it.

At step 1450, buyer may revise her RFO 10. Revision is accessible, forexample, by pressing the “I want to . . . ” button in form U900 (FIG.42).

At step 1460, buyer can choose to employ an automated bot. The botenables the buyer to automate recurring transactions. It can alert thebuyer when the transactions are supposed to be undertaken and/or it canenable the buyer to search for buyer-specified offers that areunavailable at the present time, but which are likely to appear in thefuture. The bot may run on buyer web server A500, however, it can alsorun on a dedicated server (not displayed) within core network A1200. Thechoice of using an automated bot can also be made available to the buyerat other points in the process.

At step 1470, buyer sets parameters for the bot, as illustrated inexemplary form U500 (FIG. 37). For instance, the buyer can specify,without limitation, the length of time for the bot to be active, themeans of notification of the buyer, or whether or not the transactioncan be made by the bot on the buyer's behalf At step 1500, the buyer canelect to see an analysis of final adjusted offers. The analysis isprovided to help the buyer better understand the influence of priorities20 on adjusted offers 50. It may be accessible via the “explain” buttonin form U900 (FIG. 42), or in any other suitable way.

At step 1600, analysis of adjusted offers is performed and displayed. Inone embodiment, buyer's monitor A405 displays exemplary form U1100 (FIG.45), which shows adjusted offer 50 evaluated with respect to buyer'spriorities 20. Optionally, or in another embodiment, buyer web serverA500 uses adjusted offers 50 and buyers priorities 20 to compute thecritical factors that made a particular offer inferior to thehighest-score offer. Yet, in another embodiment, buyer's monitor A405displays a table that lists all attributes of the adjusted offers 50,together with buyer's priorities 20, and explicitly shows how the scoreswere calculated.

At step 1620, the buyer can request expert suggestions. The suggestionsmay be based on numerous factors, including, without limitation, resultsof product or service testing by independent third parties,recommendations of major magazines, or reputation points given by theother users of the present invention. It can also take the form ofrecommending a complementary product, as described earlier. For example,a buyer interested in a home theater system can be informed that mostother people buying home theater systems also buy speaker stands.

At step 1640, the actual suggestion is generated and displayed. In oneembodiment, buyer web server A500 queries third party database serverA1280 for results of testing, or for third party recommendations. Italso queries buyer database server A1220 to identify other productsand/or services that are commonly purchased with the product or servicereturned in adjusted offers 50. Typical results of a suggestion searchare displayed in exemplary form U1200 (FIG. 46) on buyer's monitor A405.

At step 1700, the buyer can make a decision to purchase. This can bedone, for example, by clicking on a “buy me!” button in form U900 (FIG.42). Foregoing a purchase makes buyer web server A500 store buyer's RFO10 for potential later use. The buyer may alternatively click a “talk toa rep” button in form U900 (FIG. 42) to be connected, eithertelephonically or electronically to a seller representative, who couldpotentially answer questions in regards to the product or service inquestion.

At step 1800, the transaction is executed. In the preferred embodiment,buyer web server A500 receives buyer's billing information from buyerdatabase server A1220, and relays it to buyer interface A400 forconfirmation. For example, form U1000 (FIG. 44) may be shown on buyer'smonitor A405, asking the buyer to either confirm or modify her billingand shipping information. Upon confirmation, purchase 30 is received bybuyer web server A500 and relayed to billing server A1260 for furtherprocessing.

Billing server A1260 sends purchase 30 to HTML data interface methodserver A600, or direct database access method server A800 (possiblyutilizing a proprietary standard), or to seller web server A1000depending on the seller's setup. Purchase 30 is then received,respectively, by seller website A700, seller database A900, or sellerinterface A1100. For example, a purchase notification mediated by sellerweb server A1000 may look like that in form U3500 (FIG. 59). Purchaseannouncement 70 notifies the winning seller that a transaction has beenmade on his behalf. Also, billing server A1260 credits the seller'saccount, while applying agreed upon charges for a closed transaction.

In an alternative embodiment, if users of the present invention are notrequired to register but are required to perform the transactionin-situ, then step 1900 would consist of the buyer inputting billing andshipping information, with the rest of the process being the same asthat described above.

In yet another embodiment, if buyers complete the transaction at thewinning seller's website, then step 1900 would consist of buyer webserver A500 determining which seller was chosen by the buyer, andinstructing billing server A1260 to charge that seller a success fee.

FIG. 28 illustrates an exemplary embodiment of the process by whichauction engine server A1250 generates adjusted offers 50. The processinvolves the use of buyer's RFO 10, her priorities 20, the sellers'business rules 60, and a set of auction rules. The auction rules arepreferably specified by the Auctioneer service provider, but can also bespecified by the buyer or any other appropriate party. Optionally, thirdparty information can be used in the auction process, as explainedbelow.

At step 1210, auction engine server A1250 receives buyer's RFO 10 andher priorities 20 from buyer web server A500.

At step 1220, auction engine server A1250 queries seller rules databaseA1240, and obtains business rules from those affiliated sellers thatcould potentially satisfy RFO 10. In addition, third party informationcan be requested from third party database server A1280. For example,ratings information from a third party service (e.g., Consumer Reports)can be obtained if the buyer has limited her choices to only thoseproducts or services that have received a favorable review from such arating service. Furthermore, information from past users of the presentinvention can be obtained from buyer database A1220. For example, a listof products and services that have received fewer than 20 complaintsfrom previous buyers using the Auctioneer can be obtained if the buyerhas limited her choices to only those products or services that have notgenerated complaints by previous buyers. Simplified embodiments of thepresent invention need not include all of the various forms ofinformation. Alternatively, auction engine A1250 can just obtain thebusiness rules of sellers who satisfy all restrictions imposed by thebuyer. Auction engine A1250 may also receive constraints imposed by thebuyer on participating sellers, as specified in step 200, or limitationson bidders and auction outcomes, as specified in step 1180. Those stepsare, however, not necessary. In another embodiment, the restrictions maybe applied by buyer web server A500 after adjusted offers 50 have beengenerated, for example at step 1300.

At step 1230, the auction engine server A1250 retrieves the auctionrules previously stored on the auction engine server A1250 by theAuctioneer service provider. Alternatively, the auction engine serverA1250 can receive auction rules specified by the buyer from buyer webserver A500.

At step 1240, initial offers 40 are evaluated according to buyer'spriorities 20 and a best initial offer is determined. The evaluation mayinvolve weighting initial offers 40 by linear weights constructed frombuyer's priorities 20. Many other weighting techniques are admissible,however, such as non-linear weighting, and need not be described indetail here.

At step 1250 an adjustment of seller offers is performed. Sellerbusiness rules 60 are used to modify initial offers 40, or adjustedoffers 50 made in a previous round. Seller business rules 60 canoptionally respond based on information about the seller offers from theprevious round. More thorough specification of seller business rules 60is discussed below, with respect to FIGS. 29-30.

At step 1260, adjusted offers 50 of the present round are evaluated. Inthe preferred embodiment, the evaluation is identical to that in step1240. In alternative embodiments, however, it can be different. Theevaluation may be used, for instance, to determine whether a seller'sadjusted offer 50 is admissible. The criteria for admissibility ofadjusted offer 50 are part of the auction rules, and can be verygeneral.

At step 1270, the status of the auction is compared with auction rulesobtained in step 1230. If auction rules indicate the auction has notreached an end, it continues to loop. For example, an auction that endswhen no seller makes an improving offer may loop several times.

At step 1275, value-added product or services can optionally be added toaffiliated or unaffiliated sellers' offers.

At step 1280, the process on the auction engine server terminates, withfinal adjusted offers 50 being transmitted to buyer web server A500.

FIGS. 29 and 30 describe the process by which the seller creates andstores his business rules for the auction and obtains information, oranalysis of information, generated by the present invention. It isassumed that the seller had established an Internet connection withseller web server A1000, through seller interface A1100. Any computercapable of running Internet browser software can be used to establishthis connection.

At step 2000, the seller signs in to seller web server A1000 usingseller interface A1100. The process of signing in involves the sellersupplying any valid identification to access his account on seller rulesdatabase server A1240. The account on seller rules database server A1240had been previously created by the maintenance staff of the System,based on an affiliation agreement with the seller. The agreement can,for example, be reached using mail, email, fax, Internet formsubscription, or any other means of communication capable of supportinglegally binding agreements.

For cases in which the affiliation agreement is reached over theInternet, the seller may be presented with forms similar to U2000 (FIG.50), U2100 (FIG. 51), and U2200 (FIG. 52). Form U2000 (FIG. 50) is anexemplary overview with links to sections discussing the rights andresponsibilities accepted by the seller and the entity running thepresent invention. Form U2100 (FIG. 51) illustrates possible types ofaffiliation. As mentioned in the “Product and Pricing” section of theBackground, the present invention generates proprietary information.Different types of affiliation grant access rights to different bundlesof proprietary information. Form U2200 (FIG. 52) succinctly summarizesexemplary types of information available under each type of affiliation.In a simpler embodiment of the present invention, all sellers could haveidentical access rights to the information.

At step 2100, the seller chooses whether to view information generated,or mediated by the present invention. All affiliated sellers have accessto auction results, such as that described as near-perfect informationin the Background of the Invention. The information may range from thatwhich is also readily available from other parties, to information thatcan be, in principle, obtained in the absence of the present invention(e.g. buyers' needs, or priorities), to detailed information that isonly generated by the present invention, listed, for instance, in theright column of form U2200 (FIG. 52).

At step 2200, the seller specifies the information to view, in asuitable form displayed on seller's monitor A1115. This may include thearea of products or services, the type of information, like RFOs 10, orauction results 50, the time period, and other constraints on requestedrecords. Seller web server A1000 automatically compares the seller'srequest against his affiliation agreement obtained from seller rulesdatabase server A1240, and invalidates the request if the seller'saffiliation agreement prohibits access to the requested information. Atstep 2300, seller web server A1000 searches buyer database server A1220,or third party databases A1280 and returns results as rules analysis 90to the seller interface A1100. Forms like U3000 (FIG. 55), U3100 (FIG.56), U3200 (FIG. 57), U3400 (FIG. 58) or U3500 (FIG. 59) can be used todisplay information on individual transactions that occurred within thepresent invention. Exemplary forms U3000 (FIG. 55) and U3100 (FIG. 56)pertain to buyer's information. Some of the buyer's information may onlybe accessed with the buyer's permission, e.g., in exchange for buyerloyalty program incentives (like frequent flier points). Forms U3200(FIG. 57) and U3400 (FIG. 58) pertain to records of actual offersgenerated by the present invention, while form U3500 (FIG. 59) displaysthe terms of the offer eventually accepted by the buyer. Form U3600(FIG. 60) displays aggregate information about and analysis of auctionsoccurring during a certain time interval.

At step 2400, the seller can decide to use his business rules 60 in asimulated environment, giving him the opportunity to test them prior tocommitting to use them. Using a simulated environment helps the sellerdiscover whether his rules perform as intended.

At step 2500, the seller enters his business rules 60 into forms likeU2300 (FIG. 53) or U2400 (FIG. 54). Form U2300 (FIG. 53) represents onlyan example of the way business rules 60 can be specified. These rulescould also be driven by an electronic interface to another computerlocated on the seller's site which contains seller's own proprietaryrule based system. Different sets of specifications can be allowed indifferent categories of products. Business rules 60 are sent by sellerinterface A1100 to seller web server A1000 and passed to seller rulesdatabase server A1240, however, they are marked “simulation-only” asthey do not represent a binding commitment on the part of the seller.

At step 2600, a simulation is run inside core network A1200. In oneembodiment, auction engine A1250 obtains the last n RFOs 10 andpriorities 20 from buyer database server A1220 falling within thecategory to which the business rules apply. Auction engine A1250 thenruns n auctions employing the seller's rules 60 against other sellers'rules. In a different embodiment, auction rules 60 are treated byauction engine A1250 as valid rules, except the offers generated by themare not made visible to the buyer within returned adjusted offers 50.After the simulation ends, seller rule 60 is invalidated by seller ruledatabase server A1240.

At step 2700, auction engine server A1250 sends simulation results 70 toseller web server A1000 for further processing. Seller web server A1000passes results 70, or their analysis to seller interface A1100 wherethey are displayed on seller video monitor A1115. The results may showbasic aggregate information about how the sellers simulated rulescompared to other sellers' rules in all dimensions, as in form U3600(FIG. 60), or information on how many auctions were won, and what werethe priorities profiles to which the simulated rule most appealed.

At step 2800, the seller can continue to experiment with his businessrules in the simulation by changing the parameters.

At step 2900, the seller can modify his business rules 60 that he usesin actual (not simulated) auctions.

At step 3000, the affiliated seller enters or modifies seller businessrules 60 in form U2300 (FIG. 53), in much the same way as in thesimulated environment. The seller can adopt business rules that producedfavorable results for him in a simulation. However, the modified rulesdo not have to be based on simulation results.

At step 3100, the seller decides to make new seller business rules 60legally binding.

At step 3200, seller business rules 60 are sent to seller web serverA1000 and permanently stored within seller rules database A1240 of corenetwork A1200.

The various embodiments described above should be considered as merelyillustrative of the present invention and not in limitation thereof.They are not intended to be exhaustive or to limit the invention to theforms disclosed. Those skilled in the art will readily appreciate thatstill other variations and modifications may be practiced withoutdeparting from the general spirit of the invention set forth herein.Therefore, it is intended that the present invention be defined by theclaims which follow:

What is claimed is:
 1. A computer-implemented method of facilitating anelectronic auction between a prospective buyer and a plurality ofprospective sellers, comprising: at a computer system comprising atleast one server with one or more processors and memory: receiving intothe computer system a buyer's request for an offer; communicating therequest for an offer to at least two of the sellers; receiving offers,including terms of sale in response to the request for an offer, from atleast two of the sellers; automatically generating rating informationabout seller offers based on a plurality of predetermined criteria,wherein the plurality of predetermined criteria include at least onecriterion other than price; communicating information regarding at leastsome of the seller offers to at least one other seller; and receiving anadjusted offer from at least one of the sellers during a specifiedauction period.
 2. The method of claim 1, wherein the buyer's requestfor an offer is a recurring request that is repeated at predefinedtimes.
 3. The method of claim 1, including: at the computer system,prior to receiving the buyer's request for an offer: receiving a searchrequest from the buyer about a product and/or service that is associatedwith the buyer's request for an offer; and sending search results aboutthe product and/or service for display at a computer associated with thebuyer.
 4. The method of claim 3, including: after automaticallygenerating rating information, in response to detecting abuyer-specified event, automatically initiating a software process forcommunicating information regarding at least some of the seller offersand at least part of the rating information to the buyer.
 5. The methodof claim 1, including: sending to a computer, the computer associatedwith a representative of a respective seller that receives the requestfor an offer, information that enables launching a chat service betweenthe buyer and the representative of the respective seller.
 6. The methodof claim 1, including: sending to a computer, the computer associatedwith the buyer, information that enables the buyer to talk to arepresentative of a seller in the plurality of prospective sellers. 7.The method of claim 1, including: sending to a computer associated withthe buyer for display: information regarding at least some of the selleroffers, at least part of the rating information, and information thatenables the buyer to talk to a representative of a seller in theplurality of prospective sellers.
 8. The method of claim 7, wherein theinformation that enables the buyer to talk to the representative of theseller in the electronic auction comprises instructions for displaying abutton on the computer associated with the buyer, wherein activation ofthe button initiates a connection between the buyer and therepresentative of the seller.
 9. The method of claim 1, including:providing the buyer's identity to at least one of the sellers; andcompensating the buyer for providing the buyer's identity.
 10. Themethod of claim 9, wherein the buyer's identity is provided to a sellerthat did not win the electronic auction.
 11. A computer systemcomprising at least one server with one or more processors and memoryfor facilitating an electronic auction between a prospective buyer and aplurality of prospective sellers, wherein the computer system isconfigured to: receive into the computer system a buyer's request for anoffer; communicate the request for an offer to at least two of thesellers; receive offers, including terms of sale in response to therequest for an offer, from at least two of the sellers; automaticallygenerate rating information about seller offers based on a plurality ofpredetermined criteria, wherein the plurality of predetermined criteriainclude at least one criterion other than price; communicate informationregarding at least some of the seller offers to at least one otherseller; and receive an adjusted offer from at least one of the sellersduring a specified auction period.
 12. The computer system of claim 11,wherein the buyer's request for an offer is a recurring request that isrepeated at predefined times.
 13. The computer system of claim 11,wherein the computer system is configured to: prior to receiving thebuyer's request for an offer: receive a search request from the buyerabout a product and/or service that is associated with the buyer'srequest for an offer; and send search results about the product and/orservice for display at a computer associated with the buyer.
 14. Thecomputer system of claim 13, wherein the computer system is configuredto: after automatically generating rating information, in response todetecting a buyer-specified event, automatically initiate a softwareprocess for communicating information regarding at least some of theseller offers and at least part of the rating information to the buyer.15. The computer system of claim 11, wherein the computer system isconfigured to: send to a computer, the computer associated with arepresentative of a respective seller that receives the request for anoffer, information that enables launching a chat service between thebuyer and the representative of the respective seller.
 16. The computersystem of claim 11, wherein the computer system is configured to: sendto a computer, the computer associated with the buyer, information thatenables the buyer to talk to a representative of a seller in theplurality of prospective sellers.
 17. The computer system of claim 11,wherein the computer system is configured to: send to a computerassociated with the buyer for display: information regarding at leastsome of the seller offers, at least part of the rating information, andinformation that enables the buyer to talk to a representative of aseller in the plurality of prospective sellers.
 18. The computer systemof claim 17, wherein the information that enables the buyer to talk tothe representative of the seller in the electronic auction comprisesinstructions for displaying a button on the computer associated with thebuyer, wherein activation of the button initiates a connection betweenthe buyer and the representative of the seller.
 19. The computer systemof claim 11, wherein the computer system is configured to: provide thebuyer's identity to at least one of the sellers; and compensate thebuyer for providing the buyer's identity.
 20. The computer system ofclaim 19, wherein the buyer's identity is provided to a seller that didnot win the electronic auction.